Advanced predictive quality control strategy involving different facilities

There are many industries that use highly technological solutions to improve quality in all of their products. The steel industry is one example. Several automatic surface-inspection systems are used in the steel industry to identify various types of defects and to help operators decide whether to accept, reroute, or downgrade the material, subject to the assessment process. This paper focuses on promoting a strategy that considers all defects in an integrated fashion. It does this by managing the uncertainty about the exact position of a defect due to different process conditions by means of Gaussian additive influence functions. The relevance of the approach is in making possible consistency and reliability between surface inspection systems. The results obtained are an increase in confidence in the automatic inspection system and an ability to introduce improved prediction and advanced routing models. The prediction is provided to technical operators to help them in their decision-making process. It shows the increase in improvement gained by reducing the 40 % of coils that are downgraded at the hot strip mill because of specific defects. In addition, this technology facilitates an increase of 50 % in the accuracy of the estimate of defect survival after the cleaning facility in comparison to the former approach. The proposed technology is implemented by means of software-based, multi-agent solutions. It makes possible the independent treatment of information, presentation, quality analysis, and other relevant functions

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410

The CARMENES search for exoplanets around M dwarfs -- A deep learning approach to determine fundamental parameters of target stars

Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and predict stellar parameters such as effective temperature, surface gravity, metallicity, and rotational velocity. With this study, we firstly demonstrate the capability of deep neural networks to precisely recover stellar parameters from a synthetic training set. Secondly, we analyze the application of this method to observed spectra and the impact of the synthetic gap (i.e., the difference between observed and synthetic spectra) on the estimation of stellar parameters, their errors, and their precision. Our convolutional network is trained on synthetic PHOENIX-ACES spectra in different optical and near-infrared wavelength regions. For each of the four stellar parameters, $T_{\rm eff}$, $\log{g}$, [M/H], and $v \sin{i}$, we constructed a neural network model to estimate each parameter independently. We then applied this method to 50 M dwarfs with high-resolution spectra taken with CARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs), which operates in the visible (520-960 nm) and near-infrared wavelength range (960-1710 nm) simultaneously. Our results are compared with literature values for these stars. They show mostly good agreement within the errors, but also exhibit large deviations in some cases, especially for [M/H], pointing out the importance of a better understanding of the synthetic gap

The CARMENES search for exoplanets around M dwarfs: A deep transfer learning method to determine Teffand [M/H] of target stars

This is an Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20 K for temperature and 0.2 dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars. © The Authors 2023.CARMENES is an instrument at the Centro Astronómico Hispano en Andalucía (CAHA) at Calar Alto (Almería, Spain), operated jointly by the Junta de Andalucía and the Instituto de Astrofísica de Andalucía (CSIC). CARMENES was funded by the German Max-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de Investigaciones Científicas (CSIC), European Regional Development Fund (ERDF) through projects FICTS-2011-02, ICTS-2017-07-CAHA-4, and CAHA16-CE-3978, and the members of the CARMENES Consortium (Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Andalucía, Landessternwarte Königstuhl, Institut de Ciències de l Espai, Insitut für Astrophysik Göttingen, Universidad Complutense de Madrid, Thüringer Landessternwarte Tautenburg, Instituto de Astrofísica de Canarias, Hamburger Sternwarte, Centro de Astrobiología and Centro Astronómico Hispano-Alemán), with additional contributions by the Spanish Ministry of Economy, the German Science Foundation through the Major Research Instrumentation Programme and DFG Research Unit FOR2544 “Blue Planets around Red Stars”, the Klaus Tschira Stiftung, the states of Baden-Württemberg and Niedersachsen, and by the Junta de Andalucía. We also acknowledge the financial support from MCIN/AEI/10.13039/501100011033/ and the ERDF through projects PID2021-125627OB-C31, PID2020-112949GB-I00, and PID2019-109522GB-C51/2/3/4, and fellowship FPU15/01476; the Fundação para a Ciência e a Tecnologia through and ERDF through grants UID/FIS/04434/2019, UIDB/04434/2020, and UIDP/04434/2020, PTDC/FIS-AST/28953/2017; grant RTI2018-094614-B-I00 (SMASHING) into the “Programa Estatal de I+D+i Orientada a los Retos de la Sociedad” funded by MCIN/AEI/10.13039/501100011033, and COMPETE2020 - Programa Operacional Competitividade e Internacionalização POCI-01-0145-FEDER-028953; the University of La Laguna and the EU Next Generation funds through the Margarita Salas Fellowship from the Spanish Ministerio de Universidades UNI/551/2021-May 26; and NASA through grant NNX17AG24G.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001131-S).Peer reviewe

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

Gaia Data Release 1: Testing parallaxes with local Cepheids and RR Lyrae stars

Context. Parallaxes for 331 classical Cepheids, 31 Type II Cepheids, and 364 RR Lyrae stars in common between Gaia and the Hipparcos and Tycho-2 catalogues are published in Gaia Data Release 1 (DR1) as part of the Tycho-Gaia Astrometric Solution (TGAS). Aims. In order to test these first parallax measurements of the primary standard candles of the cosmological distance ladder, which involve astrometry collected by Gaia during the initial 14 months of science operation, we compared them with literature estimates and derived new period-luminosity (PL), period-Wesenheit (PW) relations for classical and Type II Cepheids and infrared PL, PL-metallicity (PLZ), and optical luminosity-metallicity (M V -[Fe/H]) relations for the RR Lyrae stars, with zero points based on TGAS. Methods. Classical Cepheids were carefully selected in order to discard known or suspected binary systems. The final sample comprises 102 fundamental mode pulsators with periods ranging from 1.68 to 51.66 days (of which 33 with σ Ω /Ω < 0.5). The Type II Cepheids include a total of 26 W Virginis and BL Herculis stars spanning the period range from 1.16 to 30.00 days (of which only 7 with σ Ω /Ω < 0.5). The RR Lyrae stars include 200 sources with pulsation period ranging from 0.27 to 0.80 days (of which 112 with σ Ω /Ω < 0.5). The new relations were computed using multi-band (V,I,J,K s ) photometry and spectroscopic metal abundances available in the literature, and by applying three alternative approaches: (i) linear least-squares fitting of the absolute magnitudes inferred from direct transformation of the TGAS parallaxes; (ii) adopting astrometry-based luminosities; and (iii) using a Bayesian fitting approach. The last two methods work in parallax space where parallaxes are used directly, thus maintaining symmetrical errors and allowing negative parallaxes to be used. The TGAS-based PL,PW,PLZ, and M V - [Fe/H] relations are discussed by comparing the distance to the Large Magellanic Cloud provided by different types of pulsating stars and alternative fitting methods. Results. Good agreement is found from direct comparison of the parallaxes of RR Lyrae stars for which both TGAS and HST measurements are available. Similarly, very good agreement is found between the TGAS values and the parallaxes inferred from the absolute magnitudes of Cepheids and RR Lyrae stars analysed with the Baade-Wesselink method. TGAS values also compare favourably with the parallaxes inferred by theoretical model fitting of the multi-band light curves for two of the three classical Cepheids and one RR Lyrae star, which were analysed with this technique in our samples. The K-band PL relations show the significant improvement of the TGAS parallaxes for Cepheids and RR Lyrae stars with respect to the Hipparcos measurements. This is particularly true for the RR Lyrae stars for which improvement in quality and statistics is impressive. Conclusions. TGAS parallaxes bring a significant added value to the previous Hipparcos estimates. The relations presented in this paper represent the first Gaia-calibrated relations and form a work-in-progress milestone report in the wait for Gaia-only parallaxes of which a first solution will become available with Gaia Data Release 2 (DR2) in 2018. © ESO, 2017

Gaia Data Release 1: Open cluster astrometry: performance, limitations, and future prospects

Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information.Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters.Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed.Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier HIPPARCOS-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters.Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the HIPPARCOS data, with clearly increased luminosities for older A and F dwarfs

The Gaia mission

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page. http://www.cosmos.esa.int/gai

The XXL Survey . IV. Mass-temperature relation of the bright cluster sample

Part of the first data release of the XXL Survey. Associated data is accessible via CDS and via the XXL Database hosted at IASF-MIContext. The XXL Survey is the largest survey carried out by XMM-Newton. Covering an area of 50 deg2, the survey contains ~450 galaxy clusters out to a redshift ~2 and to an X-ray flux limit of ~ 5 × 10-15 erg s-1 cm-2. This paper is part of the first release of XXL results focussed on the bright cluster sample. Aims: We investigate the scaling relation between weak-lensing mass and X-ray temperature for the brightest clusters in XXL. The scaling relation discussed in this article is used to estimate the mass of all 100 clusters in XXL-100-GC. Methods: Based on a subsample of 38 objects that lie within the intersection of the northern XXL field and the publicly available CFHTLenS shear catalog, we derive the weak-lensing mass of each system with careful considerations of the systematics. The clusters lie at 0.1 Results: The mass-temperature relation fit (M ∝ Tb) to the XXL clusters returns a slope and intrinsic scatter σlnM|T≃ 0.53; the scatter is dominated by disturbed clusters. The fit to the combined sample of 96 clusters is in tension with self-similarity, b = 1.67 ± 0.12 and σlnM|T ≃ 0.41. Conclusions: Overall our results demonstrate the feasibility of ground-based weak-lensing scaling relation studies down to cool systems of ~1 keV temperature and highlight that the current data and samples are a limit to our statistical precision. As such we are unable to determine whether the validity of hydrostatic equilibrium is a function of halo mass. An enlarged sample of cool systems, deeper weak-lensing data, and robust modelling of the selection function will help to explore these issues further. Based on observations obtained with XMM-Newton, an ESA sci- ence mission with instruments and contributions directly funded by ESA Member States and NASA. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme 089.A-0666 and LP191.A-0268.The Master catalogue is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/592/A

An online assessment & feedback approach in project management learning

This work presents an online system to facilitate the assessment and feedback in project management education. Students are involved in real-world engineering projects in order to promote professional project management learning. Thus, students share an experience in executing and managing projects and are able to put into practice different skills and competences that a project member should possess in the development of a project. The proposed system considers competence assessment through different pieces of evidence that are pertinent to each assessed competence. Information from the three main actors in learning activities (teacher, peer, and learner) is collected by means of specifically developed online forms. All the gathered evidences are considered in a weighted integration to yield a numerical assessment score of each competence that is developed for each student. Furthermore, three different types of feedback are implemented and provided several times in order to promote and improve students' learning. Data analysis from a specific academic course suggest that the presented system has a positive impact on students' academic performance. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved