Subset Of Measurements Research Articles

Segmentation of data when analyzing the state of telecommunication systems

<span lang="EN-US">The identification of abnormal situations in information and telecommunication systems is considered, based on analyze statistical information of network traffic packages. The method of identifying an anomalous situation based on segmentation of data sample is proposed. The method is aimed at using classifying algorithms that have the best quality indicators on individual data segments. The proposed method will be useful for monitoring information security systems. The method registers of factors that affect the change in the properties of targeted variables. Impact detection allows you to generate data samples, depending on current and expected situations. On the example of the NSL-KDD dataset, there was a division of many data into subset, taking into account the influence of the factors on the range of values. The processing of factors is shown using the change point detection function in the time series. With its use, a division of data sample by the final number of non-intersecting measurable subsets has been made. The results of Accuracy, Precision, F-Measure, Recall for various classifiers are shown. The proposed method allows to increase the quality indicators of classification in continuously changing operating conditions of telecommunication systems.</span>

Observability for Non-autonomous Systems

We study non-autonomous observation systems , where is a strongly measurable family of closed operators on a Banach space and is a family of bounded observation operators from to a Banach space . Based on an abstract uncertainty principle and a dissipation estimate, we prove that the observation system satisfies a final-state observability estimate in for measurable subsets . We present applications of the above result to families of uniformly strongly elliptic differential operators as well as non-autonomous Ornstein–Uhlenbeck operators on with observation operators . In the setting of non-autonomous strongly elliptic operators, we derive necessary and sufficient geometric conditions on the family of sets such that the corresponding observation system satisfies a final-state observability estimate.

Soil-building resonance effect in the urban area of Villa d’Agri (Southern Italy)

This study aims to evaluate the possible occurrence during seismic shaking of interaction effects between the shallow subsoil and the overlying buildings of the Villa d’Agri town (Southern Italy). From the geological and building heritage points of view, the investigated area represents a typical town along the Apennines chain and therefore the obtained results could be easily exported to similar urban areas. A total of 239 single-station seismic ambient noise measurements were thus performed, 154 on the urban soils and 85 within buildings. Both subsets of measurements were planned to cover the main outcropping lithologies and building typologies. By means of the Horizontal-to-Vertical Noise Spectral Ratio technique, isofrequency and isoamplitude maps have been reconstructed for the whole area. The results evaluated on the 65 tested buildings allowed to infer the empirical relationship between the first vibrational period, T(s), and their height, H(m). Using this relationship and available information on the height of each building, the first vibrational frequency was estimated for 659 buildings belonging to the urban area of Villa d'Agri. Finally, in order to assess the possible occurrence of the double resonance phenomenon, we compared the main frequency of each building with that interpolated for the underlying foundation soil. The results show that the probability of being affected by the double resonance effect is very high only for a couple of buildings, high for about 16% of the buildings, moderate for the 23% and practically null for most buildings (60%).

Beyond Geography: Social Quality Environments and Health

The concept of social quality has garnered increasing attention as a composite indicator of the well-being of societies as well as individuals embedded within them. Prior research suggests four domains of social quality: socio-economic security, social cohesion, social inclusion, and social empowerment, based on the assumption that these domains influence health and well-being. In this paper, we investigate whether and to what extent social quality environments defined with reference to the cross-cutting social quality domains reliably predict various types of health, using data collected in a municipality in Switzerland as part of a participatory action research project. We found that social inclusion had the highest predictive power for mental health and functional health, while economic security had the highest predictive power for physical capacity and overall self-rated health. Results indicate interaction among various domains of social quality for a subset of health measures. Findings suggest that environments defined as combinations of social quality domains effectively distinguish between population segments with varying levels of health. Social quality represents a promising avenue for policy and intervention development, particularly from the social determinants of health perspective, as it jointly captures the multiple domains of social well-being relevant to population health.

Disintegrations of non‐hyperbolic ergodic measures along the center foliation of DA maps

AbstractWe show that each non‐hyperbolic ergodic measure of a partially hyperbolic diffeomorphism on which is homotopic to Anosov admits a full measure subset which intersects each center leaf in at most two points.

Uniformly convergent Fourier series with universal power parts on closed subsets of measure zero

Given a closed subset E of Lebesgue measure zero on the unit circle T there is a function f on T with uniformly convergent symmetric Fourier series Sn(f,ζ)=∑k=−nnfˆ(k)ζk⇉Tf(ζ),such that for every continuous function g on E, there is a subsequence of partial power sums Sn+(f,ζ)=∑k=0nfˆ(k)ζkof f, which converges to g uniformly on E. Here fˆ(k)=∫Tζ̄kf(ζ)dm(ζ),and m is the normalized Lebesgue measure on T.

Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center

The presence of radically irregular data points (RIDPs), which are referred to as the subset of measurements that represents no or little information, can significantly degrade the performance of ellipse fitting methods. We develop an ellipse fitting method that is robust to RIDPs based on the maximum correntropy criterion with variable center (MCC-VC), where an adaptable Laplacian kernel is used. For single ellipse fitting, we formulate a non-convex optimization problem and divide it into two subproblems, one to estimate the kernel bandwidth and the other the kernel center. We design sufficiently accurate convex approximation to each subproblem that will lead to computationally efficient closed-form solutions. The two subproblems are solved in an alternate manner until convergence is reached. We also investigate coupled ellipses fitting. While there exist multiple ellipses fitting methods in the literature, we develop a coupled ellipses fitting method by exploiting the underlying special structure, where the associations between the data points and ellipses are absent in the problem. The proposed method first introduces an association vector for each data point and then formulates a non-convex mixed-integer optimization problem to establish the data associations, which is approximately solved by relaxing it into a second-order cone program. Using the estimated data associations, we then extend the proposed single ellipse fitting method to accomplish the final coupled ellipses fitting. The proposed method is shown to perform significantly better than the existing methods using both simulated data and real images.

Enhanced control structure design for an industrial off-gas system: Simple reconfigurations benefit the economy

For control structure design of the industrial off-gas benchmark system, application of the Skogestad's state-of-art design procedure has suggested the scrubber inlet pressure (Psi in the roaster) and one of the fan speeds (Nfan1 or N_fan2 in the furnace) as the self-optimizing controlled variables CVs. In this study, we stress and advocate the gSOC-plus-BAB approach as an enhanced design toolkit for the classical and systematical design procedure. The gSOC (global self-optimizing control) is able to efficiently solve measurement combinations as CVs with improved economic performances, while the BAB (branch and bound) algorithm serves to fast screen promising measurement subsets for large-scale problems. Using the enhanced design for the off-gas system, our new findings are to control the combination of roaster's ID fan outlet pressures, 0.494PIDfan1+0.506PIDfan2 (setpoint: −257.04 Pa), and the furnace's fan pressure difference, PIDfan1- PIDfan2 (setpoint: 0). Such simple reconfigurations can dramatically reduce the average economic loss by 53.3% for the roaster and even achieve perfect optimal control for the furnace. Both steady state and dynamic evaluations are carried out to validate the reconfigured control structures.© 2017 Elsevier Inc. All rights reserved.

ПРОСТРАНСТВА ДИФФЕРЕНЦИАЛЬНЫХ ФОРМ СО СТОХАСТИЧЕСКИМИ КОМПЛЕКСНОЗНАЧНЫМИ КОЭФФИЦИЕНТАМИ

This article investigates the construction of spaces of differential forms with coefficients which are stochastic complex-valued K-processes. A complete probability space and complex-valued random variables on measurable subsets of this space are considered, and continuous random complexvalued K- processes are also introduced. Next, we construct spaces of differential forms with coefficients in the form of such stochastic complex-valued K-processes.

QUALITY OF LIFE IN ADULTS LIVING WITH COGNITIVE IMPAIRMENT: DEVELOPMENT OF A UNIVERSAL MEASURE

Abstract Quality of life is one of the most important and holistic measures of perceived overall well-being across the lifespan. For older adults living with cognitive impairment, quality of life has been conceptualized as consisting of an array of intertwined domains, such as relationship quality, physical health and ability, and opportunities for roles and activities. A subset of measures have been developed to gauge quality of life in older adults living with cognitive impairment, yet no “gold standard” in measurement has emerged. Quality of Life – Alzheimer’s Disease (QOL-AD) is one of the most commonly used and long-standing measures, yet it was developed in 2002 and has yet to be updated to reflect the evolution of our understanding of quality of life in adults living with cognitive impairment. Drawing from the literature on adults living with intellectual and developmental disabilities (IDD), the researchers identified several domains and items to update and expand the applicability of the QOL-AD measure. The proposed revised measure, Quality of Life – Alzheimer’s Disease/Intellectual and Developmental Disabilities (QOL-AD/IDD), is intended for adults living with cognitive impairment regardless of age or cause of impairment (e.g., dementia, IDD). The QOL-AD/IDD retains the format of the original measure, most notably the useful prompts that accompany each item and the supplemental proxy measure for caregivers. In this presentation, the researchers will discuss the development of the QOL-AD/IDD and ongoing (e.g., Delphi review) and planned steps to evaluate the reliability and validity of this promising measure.

A new crest height distribution for nonlinear and breaking waves in varying water depths

The statistical distribution of zero-crossing crest heights represents a critical design input for a wide range of engineering applications. The present paper describes the development and validation of a new crest height model, suitable for application across a broad range of water depths. The purpose of this model is two-fold: first, to describe the amplifications of the largest crest heights arising due to nonlinear interactions beyond a second-order of wave steepness, and second, to incorporate the dissipative effects of wave breaking. Although these two effects act counter to each other, there is substantial evidence to suggest departures from existing models based upon weakly nonlinear second-order theory; the latter corresponding to current design practice. The proposed model has been developed on the basis of a significant collection of experimental results and a small subset of field measurements. It incorporates effects arising at different orders of nonlinearity as well as wave breaking in a compact formulation and covers a wide range of met-ocean conditions. Importantly, the new model has been independently validated against a very extensive database of experimental and field measurements. Taken together, these include effective water depths ranging from shallow water (kpd≈0.5) to deep water (kpd>3) and sea-state steepnesses covering mild, severe and extreme conditions. The new model is shown to provide a significant improvement in crest height predictions over existing methods. This is particularly evident in the steepest, most severe sea-states which inevitably form the basis of design calculations.

Some properties of Vitali sets

Abstract If V 1 , … , V n {V_{1},\dots,V_{n}} are translations of a Vitali set by rational numbers, then we prove that ⋃ i = 1 n V i {\bigcup_{i=1}^{n}V_{i}} contains no measurable subset of positive measure. This provides a decomposition of ℝ {{\mathbb{R}}} as a countable union of disjoint sets, any finite union of which has Lebesgue inner measure zero. As a consequence, we present a function δ : ℝ → ( 0 , + ∞ ) {\delta:{\mathbb{R}}\rightarrow(0,+\infty)} for which there is no measurable function f : ℝ → ℝ {f:{\mathbb{R}}\rightarrow{\mathbb{R}}} satisfying 0 < f ≤ δ {0<f\leq\delta} , on any measurable set of positive Lebesgue measure.

Inverse modeling of wind turbine drivetrain from numerical data using Bayesian inference

Wind turbine drivetrain systems experience occasional failure in their operational life-time. Dynamic loads in these systems often differ from those predicted in the design phase, which may lead to incipient faults and damage eventually resulting to unpredicted failures. Faults in a system cause changes in the system dynamic properties. Therefore, a calibrated dynamic model can be used for fault diagnosis of the drivetrain system. The current paper implements a Bayesian inference method for physics-based model updating for the first time to estimate the parameters of a physics-based wind turbine drivetrain model as well as the unknown input loads using measurement data. An information-theoretic approach is adopted to study the identifiability of different model parameters given different subsets of drivetrain measurements. The 5 MW NREL wind turbine is used as a case study, and its drivetrain is modeled by a 5-mass torsional model. Torsional shaft stiffnesses and rotor mass moment of inertia are the parameters reflecting dynamics of the drivetrain and are assumed unknown. Moreover, the aerodynamics input torque applied to the drivetrain system is assumed as unknown. The Bayesian inference method is used to estimate jointly the unknown model parameters and aerodynamic input torque using numerically simulated measurement data. The case studies show that the proposed model updating approach can accurately estimate the low-speed shaft stiffness, rotor mass moment of inertia, and the time history of aerodynamic torque jointly using only measurements from the generator including generator torque and rotational speed.

On the existence of optimizers for time–frequency concentration problems

We consider the problem of the maximum concentration in a fixed measurable subset $$\Omega \subset {\mathbb {R}}^{2d}$$ of the time-frequency space for functions $$f\in L^2({\mathbb {R}}^{d})$$ . The notion of concentration can be made mathematically precise by considering the $$L^p$$ -norm on $$\Omega $$ of some time–frequency distribution of f such as the ambiguity function A(f). We provide a positive answer to an open maximization problem, by showing that for every subset $$\Omega \subset {\mathbb {R}}^{2d}$$ of finite measure and every $$1\le p<\infty $$ , there exists an optimizer for $$\begin{aligned} \sup \{\Vert A(f)\Vert _{L^p(\Omega )}:\ f\in L^2({\mathbb {R}}^{d}),\ \Vert f\Vert _{L^2}=1 \}. \end{aligned}$$ The lack of weak upper semicontinuity and the invariance under time-frequency shifts make the problem challenging. The proof is based on concentration compactness with time–frequency shifts as dislocations, and certain integral bounds and asymptotic decoupling estimates for the ambiguity function. We also discuss the case $$p=\infty $$ and related optimization problems for the time correlation function, the cross-ambiguity function with a fixed window, and for functions in the modulation spaces $$M^q({\mathbb {R}}^{d})$$ , $$0<q<2$$ , equipped with continuous or discrete-type (quasi-)norms.

Low-Variance Parameter Estimation Approach for Real-Time Optimization of Noisy Process Systems

Uncertainty is inherent to the measurement and modeling of process systems, where it can have significant impacts on the efficacy of optimization techniques. This work proposes a scheme to address uncertainty as it pertains to real-time optimization (RTO), where noisy measurements are used to estimate model parameters and account for model uncertainty. The parameter estimation (PE) step that accompanies RTO requires plant measurements that are often noisy; this can cause the propagation of noise to the parameter estimates, which may result in poor RTO performance. An information content (IC) metric for choosing the most information-rich measurements and an algorithm to select a favorable subset of measurements, as well as filtering for erroneous parameters, are proposed in this work to improve the PE problem performance. The resulting low-variance PE (lv-PE) algorithm yields parameter estimates that are closer to the true parameter values over many RTO periods. The proposed scheme is tested against a regular RTO/PE on a forced circulation evaporator and the Williams–Otto CSTR. The former case displays the effect of the proposed scheme in avoiding constraint violations, while the latter case shows the economic improvements that the proposed scheme can yield. Both case studies show a reduction in estimate variability with respect to the traditional PE approach; thus, the proposed framework is attractive for the optimization of noisy process systems.

Revealing non-Hermitian band structure of photonic Floquet media.

Periodically driven systems are ubiquitously found in both classical and quantum regimes. In the field of photonics, these Floquet systems have begun to provide insight into how time periodicity can extend the concept of spatially periodic photonic crystals and metamaterials to the time domain. However, despite the necessity arising from the presence of nonreciprocal coupling between states in a photonic Floquet medium, a unified non-Hermitian band structure description remains elusive. We experimentally reveal the unique Bloch-Floquet and non-Bloch band structures of a photonic Floquet medium emulated in the microwave regime with a one-dimensional array of time-periodically driven resonators. These non-Hermitian band structures are shown to be two measurable distinct subsets of complex eigenfrequency surfaces of the photonic Floquet medium defined in complex momentum space.

Generic Identifiability of the DINA Model and Blessing of Latent Dependence.

Cognitive diagnostic models are a powerful family of fine-grained discrete latent variable models in psychometrics. Within this family, the DINA model is a fundamental and parsimonious one that has received significant attention. Similar to other complex latent variable models, identifiability is an important issue for CDMs, including the DINA model. Gu and Xu (Psychometrika 84(2):468-483, 2019) established the necessary and sufficient conditions for strict identifiability of the DINA model. Despite being the strongest possible notion of identifiability, strict identifiability may impose overly stringent requirements on designing the cognitive diagnostic tests. This work studies a slightly weaker yet very useful notion, generic identifiability, which means parameters are identifiable almost everywhere in the parameter space, excluding only a negligible subset of measure zero. We propose transparent generic identifiability conditions for the DINA model, relaxing existing conditions in nontrivial ways. Under generic identifiability, we also explicitly characterize the forms of the measure-zero sets where identifiability breaks down. In addition, we reveal an interesting blessing-of-latent-dependence phenomenon under DINA-that is, dependence between the latent attributes can restore identifiability under some otherwise unidentifiable [Formula: see text]-matrix designs. The blessing of latent dependence provides useful practical implications and reassurance for real-world designs of cognitive diagnostic assessments.

A generalized approach to characterize optical properties of natural objects

Abstract To understand the diversity of ways in which natural materials interact with light, it is important to consider how their reflectance changes with the angle of illumination or viewing and to consider wavelengths beyond the visible. Efforts to characterize these optical properties, however, have been hampered by heterogeneity in measurement techniques, parameters and terminology. Here, we propose a standardized set of measurements, parameters and terminology to describe the optical properties of natural objects based on spectrometry, including angle-dependent effects, such as iridescence and specularity. We select a set of existing measurements and parameters that are generalizable to any wavelength range and spectral shape, and we highlight which subsets of measures are relevant to different biological questions. As a case study, we have applied these measures to 30 species of Christmas beetles, in which we observed previously unrealized diversity in visible and near-infrared reflectance. As expected, reflection of short wavelengths was associated with high spectral purity and angle dependence. In contrast to simple, artificial structures, iridescence and specularity were not strongly correlated, highlighting the complexity and modularity of natural materials. Species did not cluster according to spectral parameters or genus, suggesting high lability of optical properties. The proposed standardization of measures and parameters will improve our understanding of biological adaptations for manipulating light by facilitating the systematic comparison of complex optical properties, such as glossy or metallic appearances and visible or near-infrared iridescence.

Bures–Wasserstein geometry for positive-definite Hermitian matrices and their trace-one subset

In his classical argument, Rao derives the Riemannian distance corresponding to the Fisher metric using a mapping between the space of positive measures and Euclidean space. He obtains the Hellinger distance on the full space of measures and the Fisher distance on the subset of probability measures. In order to highlight the interplay between Fisher theory and quantum information theory, we extend this construction to the space of positive-definite Hermitian matrices using Riemannian submersions and quotient manifolds. The analog of the Hellinger distance turns out to be the Bures–Wasserstein (BW) distance, a distance measure appearing in optimal transport, quantum information, and optimisation theory. First we present an existing derivation of the Riemannian metric and geodesics associated with this distance. Subsequently, we present a novel derivation of the Riemannian distance and geodesics for this metric on the subset of trace-one matrices, analogous to the Fisher distance for probability measures.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway.

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.