Elastically restrained Bernoulli-Euler beams applied to rotary machinery modelling

Facing the lateral vibration problem of a machine rotor as a beam on elastic supports in bending, the authors deal with the free vibration of elastically restrained Bernoulli-Euler beams carrying a finite number of concentrated elements along their length. Based on Rayleigh's quotient, an iterative strategy is developed to find the approximated torsional stiffness coefficients, which allows the reconciliation between the theoretical model results and the experimental ones, obtained through impact tests. The mentioned algorithm treats the vibration of continuous beams under a determined set of boundary and continuity conditions, including different torsional stiffness coefficients and the effect of attached concentrated masses and rotational inertias, not only in the energetic terms of the Rayleigh's quotient but also on the mode shapes, considering the shape functions defined in branches. Several loading cases are examined and examples are given to illustrate the validity of the model and accuracy of the obtained natural frequencies

Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.info:eu-repo/semantics/publishedVersio

A Study of Generalization and Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Generalization and Fitness Landscapes for Neuroevolution. IEEE Access, 8, 108216-108234. [9113453]. https://doi.org/10.1109/ACCESS.2020.3001505Fitness landscapes are a useful concept for studying the dynamics of meta-heuristics. In the last two decades, they have been successfully used for estimating the optimization capabilities of different flavors of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have not been used for studying the performance of machine learning algorithms on unseen data, and they have not been applied to studying neuroevolution landscapes. This paper fills these gaps by applying fitness landscapes to neuroevolution, and using this concept to infer useful information about the learning and generalization ability of the machine learning method. For this task, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations used to evolve them. To characterize fitness landscapes, we study autocorrelation, entropic measure of ruggedness, and fitness clouds. Also, we propose the use of two additional evaluation measures: density clouds and overfitting measure. The results show that these measures are appropriate for estimating both the learning and the generalization ability of the considered neuroevolution configurations.publishersversionpublishe

A Study of Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Fitness Landscapes for Neuroevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020: Conference Proceedings [9185783] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185783Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution. This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method. More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them. To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness. The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.preprintpublishe

ARCHI: pipeline for light curve extraction of CHEOPS background star

High precision time series photometry from space is being used for a number of scientific cases. In this context, the recently launched CHEOPS (ESA) mission promises to bring 20 ppm precision over an exposure time of 6 hours, when targeting nearby bright stars, having in mind the detailed characterization of exoplanetary systems through transit measurements. However, the official CHEOPS (ESA) mission pipeline only provides photometry for the main target (the central star in the field). In order to explore the potential of CHEOPS photometry for all stars in the field, in this paper we present archi, an additional open-source pipeline module{\dag}to analyse the background stars present in the image. As archi uses the official Data Reduction Pipeline data as input, it is not meant to be used as independent tool to process raw CHEOPS data but, instead, to be used as an add-on to the official pipeline. We test archi using CHEOPS simulated images, and show that photometry of background stars in CHEOPS images is only slightly degraded (by a factor of 2 to 3) with respect to the main target. This opens a potential for the use of CHEOPS to produce photometric time series of several close-by targets at once, as well as to use different stars in the image to calibrate systematic errors. We also show one clear scientific application where the study of the companion light curve can be important for the understanding of the contamination on the main target.Comment: 14 pages, 13 figures, accepted for publication in MNRAS, all code available at https://github.com/Kamuish/arch

Deposition of conductive materials on textile and polymeric flexible substrates

This paper describes the study, analysis and selection of textile and similar materials to be used as flexible substrates for thin conductive film deposition, in the context of integrating electronics into textiles. Kapton® polyimide was chosen as reference substrate material, was characterized regarding mechanical and electrical properties and was used as a basis for a comparison with several textile substrates. Samples were fabricated using physical vapour deposition (thermal evaporation) to deposit a thin layer of aluminium on top of Kapton and textile substrates. The measurement of electrical resistance of the thin aluminum films was carried out using the Kelvin method. To characterize the mechanical behaviour of the substrate and aluminum film, several mechanical tests were performed and results were compared between Kapton and these textile materials. The chemical composition of the textile substrates and aluminum films as well as the continuity of the films was characterized. This selection process identified the material that was closer to the behaviour of polyimide, a flexible, but non-elastic woven textile coated on both sides with PVC.FEDER funds in COMPETE program and by FCT, in the project FCOMP-01-0124-FEDER-02267

Indoor air quality assessment in grocery stores

FCT_UIDB/05608/2020. FCT_UIDP/05608/2020.Indoor Air Quality (IAQ) is a public and occupational health concern, as we are exposed to air pollutants daily given that we spend a great amount of our time in indoor environments. IAQ can be affected by numerous factors, from outdoor pollutants that get indoors through ventilation to building materials, furnishings, and activities carried by the building occupants. Exposure to air pollutants has been linked to a panoply of adverse effects on our health, well-being, and performance. The aim of this study was to assess the IAQ in grocery stores (GSs) in the municipality of Cascais in the Lisbon metropolitan area (Portugal) to characterize the workers’ occupational exposure to air pollutants. The study was conducted in 13 small “family” grocery stores. The IAQ monitoring campaign was conducted using low-cost sensor technologies and focused on several parameters, namely: carbon dioxide (CO2), volatile organic compounds (VOCs), particulate matter (PM10 and PM2.5), temperature (T), relative humidity (RH). Overall, the IAQ of the studied GSs complied with Portuguese legislation, except for PM2.5, where 23% of GSs presented levels above the established limit value. The mean inhaled dose of workers during a workday was estimated to be 157.7 ± 57.2 μg for PM2.5 and 165.8 ± 56.0 μg for PM10. The IAQ assessment allowed the identification of the best strategies to improve worker experience in indoor workplace environments.info:eu-repo/semantics/publishedVersio