Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements

Observations of the Sun from the Earth are always limited by the presence of the atmosphere, which strongly disturbs the images. A solution to this problem is to place the telescopes in space satellites, which produce observations without any (or limited) atmospheric aberrations. However, even though the images from space are not affected by atmospheric seeing, the optical properties of the instruments still limit the observations. In the case of diffraction limited observations, the PSF establishes the maximum allowed spatial resolution, defined as the distance between two nearby structures that can be properly distinguished. In addition, the shape of the PSF induce a dispersion of the light from different parts of the image, leading to what is commonly termed as stray light or dispersed light. This effect produces that light observed in a spatial location at the focal plane is a combination of the light emitted in the object at relatively distant spatial locations. We aim to correct the effect produced by the telescope's PSF using a deconvolution method, and we decided to apply the code on Hinode/SP quiet Sun observations. We analyze the validity of the deconvolution process with noisy data and we infer the physical properties of quiet Sun magnetic elements after the deconvolution process.Comment: 14 pages, 9 figure

Clustering Techniques Performance for the Coordination of Adaptive Overcurrent Protections

Inclusion of distributed generation and topological changes in a network originate several operating scenarios. For this reason, techniques that adjust the configuration of overcurrent relays have been developed in order to provide protection coordination strategies capable of operating in different schemes. However, the adjustments allowed by these devices are limited. Thus, scenario grouping techniques are proposed to reduce the number of required configurations. This paper aims to evaluate the performance of different grouping techniques with input parameters for coordination strategies of electrical overcurrent protections, where it is required to associate the different modes of operation of a distribution network. For the clustering process, unsupervised learning techniques such as K-means, K-medoids and Agglomerative Hierarchical Clustering were employed. Additionally, for the input characteristics, fault currents, nominal currents and other parameters obtained from the electrical system were taken into account. From the results obtained when evaluating different combinations of techniques and inputs, it is important to mention that the characteristics that describe the different modes of operation necessary for the grouping are decisive for the coordination strategies of electrical protections and that it is not possible to establish a significant difference between the clustering techniques evaluated. Lastly, the combination that presents the best performance was K-means: Manhattan and maximum short-circuit phase currents per relay with a sum of operation time of 428.72s and zero restriction violation. © 2022 IEEE

Monitoreo de Condición en Motores de Combustión Interna Monocilíndricos con Base en Adquisición y Procesamiento de Señales Experimentales

In recent years, condition monitoring based on signal analysis has become a valuable tool for the diagnosis of internal combustion engines. In this paper the experimental design for the ICE monitoring condition, based on signal analysis, is presented. The experimental configuration was development for the analysis of signals from ICE in order to monitor their condition. The conduced case study consists on the monitoring condition of a single-cylinder engine, operating under regular conditions and different speeds. The instrumentation, the adquisition systems as well as the signals analysis are also presented. The adquired signals were: engine block vibration, in-cylinder pressure and crankshaft speed. The mentioned signals were analyzed and processed by FFT and Rigid Regression. It was possible to obtain the frequency spectrum of the vibration signal and reconstruct the in-cylinder pressure of the single-cylinder engine. The presented configuration can be taken as a basis for the evaluation of others engines and for improving the schemes of monitoring condition.Keywords: Internal combustion engines, condition monitoring, signal acquisition, signal processing

Effective medium electrical response model of carbon nanotubes cement-based composites

The electrical properties of carbon nanotubes (CNT) cement-based composites have been modeled in previous works by circuit models or homogenization theories. An alternative approach is the use of an effective medium theory with induced polarization: In this work, a new model based on the generalized effective medium theory of induced polarization (GEMTIP) with cylindrical inclusions is proposed. The presented results and discussion show its applicability to interpret the electrical impedance spectra of cylindrical cement samples doped with multi-walled CNTs (MWCNTs). The MWCNTs were dispersed in different media: one nonionic surfactant, two superplasticizers, a cationic type polycarboxylate ether, and an anionic type naphthalene sulfonate. Particle dispersion and their sizes were analyzed by Ultraviolet–Visible (UV–Vis) spectroscopy, and Scanning Electron Microscopy (SEM) measurements. Two electrode electrical impedance spectra were measured and analyzed by circuital models and the proposed GEMTIP model. The results demonstrate the efficiency of the proposed model in describing the Alternating Current (AC) response of cement/CNT composites irrespective of the dispersant agent used to elaborate the samples.CBUAConsejería de Transformación Económica, Conocimiento, Empresas Universidades de la Junta de Andalucía P18-RT-3128MinCiencias 82779Piezoresistividad en Pasta de Cemento con Adición de Nanopartículas de Oro o Materiales CarbonososUniversidad de GranadaInstituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterio

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications. © 2013 State Grid Electric Power Research Institute

Piezoelectric composite cements: Towards the development of self-powered and self-diagnostic materials

Piezoresistivity is the most commonly used sensing principle in cement-based smart composites for strainmonitoring applications. Nonetheless, the need for external electric power to conduct electrical resistivity measurements restricts the scalability of this technology, especially when implemented in remote structures. To address this issue, this manuscript thoroughly analyses the piezoelectric properties of cement composites doped with reduced graphene oxide (rGO) and evaluates their potential as self-powered strain sensors. To do so, a comprehensive methodology involving voltammetry measurements, open circuit potential determination, and uniaxial compression testing is developed to determine the piezoelectric coefficients of charge �33 and voltage �33. Furthermore, a novel circuital model for signal processing of the electromechanical response is developed and experimentally validated in terms of time series of output voltage, resistance, and the generated electric power. The developed methodology is applied to laboratory samples manufactured following two different filler dispersion methods. The presented results evidence that samples prepared by ultrasonic cleaner dispersion achieve optimal properties, with a piezoelectric charge coefficient of 1122.28 ± 246.67 pC/N, about 47 times greater than previously reported composites in the literature. Unlike piezoresistive cement-based composites, a remarkable nonlinear correlation between the fractional change in the intrinsic resistance of the material and the applied mechanical strain has been observed. Instead, a considerable linearity (R 2 = 0.96) between the externally applied mechanical strain and the generated (piezoelectric) electric power has been found, which suggests the great potential of the latter for conducting off-the-grid strain monitoring applications

Cement-Based Piezoelectricity Application: A Theoretical Approach

The linear theory of piezoelectricity has widely been used to evaluate the material constants of single crystals and ceramics, but what happens with amorphous structures that exhibit piezoelectric properties such as cement-based? In this chapter, we correlate the theoretical and experimental piezoelectric parameters for small deformations after compressive stress–strain, open circuit potential, and impedance spectroscopy on cement-based. Here, in detail, we introduce the theory of piezoelectricity for large deformations without including a functional for the energy; also, we show two generating equations in terms of a free energy’s function for later it will be reduced to constitutional equations of piezoelectricity for infinitesimal deformations. Finally, here is shown piezoelectric and electrical parameters of gold nanoparticles mixed to cement paste: the axial elasticity parameter Y=323.5±75.3kN/m2, the electroelastic parameter γ=−20.5±6.9mV/kN, and dielectric constant ε=939.6±82.9ε0F/m, which have an interpretation as linear theory parameters sijklD, gkij and εikT discussed in the chapter

Utjecaj trajanja in vitro sazrijevanja i inkubacije s aktivirajućim čimbenikom na kapacitet izlijeganja goveđih partenota - kratko priopćenje

The period of both in vitro maturation (IVM) and incubation with oocyte activators affects the blastocyst yield following parthenogenetic activation (PA). Nevertheless, it is still unknown how these conditions impact the expansion and hatching rates of bovine parthenogenetic blastocysts. The objective of this study was to assess the influence of the duration of IVM and exposure to the activating agent, 6-dimethylaminopurine (6-DMAP), on a number of developmental parameters in bovine parthenotes, including: Cleavage, blastocyst formation, expansion, and hatching. Slaughterhouse oocytes were subjected to different periods of IVM. Subsequently, eggs were first parthenogenetically activated for five minutes with ionomycin and then incubated for distinct lengths of time with a second activator, 6-DMAP. The treatments were: a) Control: 22 h IVM/4 h 6-DMAP; b) 22 h IVM/5 h 6-DMAP; c) 24 h IVM/4 h 6-DMAP; and d) 24 h IVM/5 h 6-DMAP. Developmental stages were evaluated at day 4 and day 8 of in vitro culture (IVC). No differences were detected in most developmental parameters. However, the duration of IVM and incubation with 6-DMAP significantly affected (P<0.05) hatching capacity considering the number of blastocysts (Hatch./Blast.). Also, this same variable was higher (P<0.05) in group b) 22 h IVM/5 h 6-DMAP (45.89 ± 12.59%), as compared to c) 24 h IVM/4 h 6-DMAP (6.67 ± 6.67%). In conclusion, the length of IVM and incubation with 6-DMAP influenced parthenogenetic development, where 22 h IVM/5 h 6-DMAP was the condition producing the highest Hatch./Blast. rate in bovine parthenotes.Vrijeme in vitro sazrijevanja (IVM) i vrijeme inkubacije s aktivatorima oocista utječu na stvaranje blastocista nakon partenogenetske aktivacije (PA). Ipak, još uvijek se ne zna kako navedeno utječe na ekspanziju i stopu izlijeganja goveđih partenogenetskih blastocista. Cilj rada bio je istražiti utjecaj trajanja IVM i izloženosti aktivirajućem čimbeniku 6-dimethylaminopurinu (6-DMAP) na više razvojnih parametara u goveđih partenota, uključujući diobu, formiranje blastociste, ekspanziju i izlijeganje. Oocite prikupljene u klaonicama bile su podvrgnute različitom trajanju IVM. Nakon toga jajašca su prvo partenogenetski aktivirana s ionomicinom kroz 5 minuta i nakon toga inkubirana tijekom određenih vremenskih razdoblja sa drugim aktivatorom, 6-DMAP. Protokoli po istraženim skupinama bili su sljedeći: a) kontrolna skupina 22 h IVM/4 h 6-DMAP, b) skupina 22 h IVM/5 h 6-DMAP, c) skupina 24 h IVM/4 h 6-DMAP i d) skupina 24 h IVM/5 h 6-DMAP. Razvojni stadiji in vitro kulture (IVC) procijenjivani su 4. i 8. dan. Za većinu razvojnih parametara nisu utvrđene razlike između istraženih skupina. Ipak, trajanja IVM i inkubacije sa 6-DMAP znakovito su utjecali (P<0,05) na kapacitet izlijeganja kad se u obzir uzme broj blastocista (izlijeganja/ blasociste). Također, isti pokazatelji bili su viši (P<0,05) u skupini b) 22 h IVM/5 h 6-DMAP (45,89 ± 12,59 %) u odnosu na skupinu c) 24 h IVM/4 h 6-DMAP (6,67 ± 6,67 %). Zaključno, trajanje IVM i inkubacije sa 6-DMAP utjecali su na partenogenetski razvoj, pri čemu je 22 h IVM/5 h 6-DMAP kombinacija koja u goveđih partenota proizvodi najvišu stopu za pokazetelj izlijeganje/blasociste

Estimación de la curva de presión en la cámara de combustión de MCI monocilíndricos a partir del análisis de vibraciones

En este artículo se diseña e implementa un sistema para la estimación de la curva de presión en la cámara de combustión en un MCI mediante el análisis de las vibraciones mecánicas provenientes del bloque del motor. Para la generación de la base de datos, se capturaron tres tipos de señales: Vibración en el bloque, velocidad de rotación del cigüeñal y presión en la cámara de combustión (como referencia). Se evaluó el motor trabajando con gasolina a diferentes regímenes de velocidad (1100, 1500, 2000 y 2500 rpm) en condiciones normales. Las señales de vibración fueron caracterizadas mediante el uso de transformadas rápidas de Fourier (FFT) y transformadas en tiempo corto de Fourier (STFT). Para estimar la curva de presión, se hizo uso de regresión rígida con Kernel utilizando una función gaussiana. Se valida la estimación de la curva de presión comparándola con la curva obtenida por medio del sensor de presión. Se utilizó una validación cruzada para medir la precisión del sistema. Los resultados demostraron que la estimación de la curva de presión en la cámara de combustión es apropiada, cuando el MCI trabaja a diferentes velocidades en condiciones normales de operación.Eje: Eficiencia energética.Facultad de Ingenierí

Estimación de la curva de presión en la cámara de combustión de MCI monocilíndricos a partir del análisis de vibraciones

En este artículo se diseña e implementa un sistema para la estimación de la curva de presión en la cámara de combustión en un MCI mediante el análisis de las vibraciones mecánicas provenientes del bloque del motor. Para la generación de la base de datos, se capturaron tres tipos de señales: Vibración en el bloque, velocidad de rotación del cigüeñal y presión en la cámara de combustión (como referencia). Se evaluó el motor trabajando con gasolina a diferentes regímenes de velocidad (1100, 1500, 2000 y 2500 rpm) en condiciones normales. Las señales de vibración fueron caracterizadas mediante el uso de transformadas rápidas de Fourier (FFT) y transformadas en tiempo corto de Fourier (STFT). Para estimar la curva de presión, se hizo uso de regresión rígida con Kernel utilizando una función gaussiana. Se valida la estimación de la curva de presión comparándola con la curva obtenida por medio del sensor de presión. Se utilizó una validación cruzada para medir la precisión del sistema. Los resultados demostraron que la estimación de la curva de presión en la cámara de combustión es apropiada, cuando el MCI trabaja a diferentes velocidades en condiciones normales de operación.Eje: Eficiencia energética.Facultad de Ingenierí