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  1. AU="Turias, Ignacio"
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  1. Article: A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels.

    Contreras-Fortes, Julia / Rodríguez-García, M Inmaculada / Sales, David L / Sánchez-Miranda, Rocío / Almagro, Juan F / Turias, Ignacio

    Materials (Basel, Switzerland)

    2023  Volume 17, Issue 1

    Abstract: Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold ...

    Abstract Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (
    Language English
    Publishing date 2023-12-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma17010147
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain)

    González-Enrique, Javier / Ruiz-Aguilar, Juan Jesús / Moscoso-López, José Antonio / Urda, Daniel / Turias, Ignacio J.

    Stochastic environmental research and risk assessment. 2021 Oct., v. 35, no. 10

    2021  

    Abstract: This study presents a comparison between sixteen filter ranking methods applied to a real air pollution problem. Adaptations of the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm to use the Spearman's rank correlation, the kernel canonical ... ...

    Abstract This study presents a comparison between sixteen filter ranking methods applied to a real air pollution problem. Adaptations of the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm to use the Spearman's rank correlation, the kernel canonical correlation analysis, the Hilbert–Schmidt independence criterion, correntropy, the Pearson's correlation and the distance correlation are included among them. These methods were compared by estimating the hourly NO₂ concentrations at three monitoring stations located in the Bay of Algeciras (Spain). The estimation models were generated using Bayesian regularized artificial neural networks. Different estimation cases were tested for each ranking method. Finally, results were statistically compared to determine which filter ranking strategy produced the best performing model in each case. The proposed estimation scenarios showed how mRMR methods had better results than all the remaining methods when a small number of features was selected. However, their advantage was not so evident when the number of selected features increased. Results from the proposed mRMR methods were promising, especially in the case of the distance correlation mRMR, the kernel canonical correlation analysis mRMR and the Spearman's rank correlation mRMR. These ranking methods performed better than the original mRMR algorithm that employs mutual information internally.
    Keywords Bayesian theory ; air pollution ; algorithms ; multivariate analysis ; research ; risk assessment ; seeds ; Spain
    Language English
    Dates of publication 2021-10
    Size p. 1999-2019.
    Publishing place Springer Berlin Heidelberg
    Document type Article
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-021-01992-4
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO

    González-Enrique, Javier / Ruiz-Aguilar, Juan Jesús / Moscoso-López, José Antonio / Urda, Daniel / Deka, Lipika / Turias, Ignacio J

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 5

    Abstract: This study aims to produce accurate predictions of the ... ...

    Abstract This study aims to produce accurate predictions of the NO
    Language English
    Publishing date 2021-03-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21051770
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Analysis of the global and technical efficiencies of Major Spanish container ports

    Gil-Ropero, Antonio / Cerban, Mar / Turias, Ignacio J

    International journal of transport economics : IJTE Bd. XLII.2015, 3 (sep.), S. 377-407

    2015  

    Author's details Antonio Gil-Ropero; Mar Cerban; Ignacio J. Turias
    Keywords Efficiency ; Data Envelopment Analysis ; Container Ports ; Terminals ; Logistics
    Language English
    Size graph. Darst.
    Publisher Serra
    Publishing place Pisa [u.a.]
    Document type Article
    ZDB-ID 165281-3
    ISSN 0391-8440
    Database ECONomics Information System

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  5. Article: Prediction models of CO, SPM and SO(2) concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy.

    Turias, Ignacio J / González, Francisco J / Martin, Ma Luz / Galindo, Pedro L

    Environmental monitoring and assessment

    2007  Volume 143, Issue 1-3, Page(s) 131–146

    Abstract: The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon ...

    Abstract The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO(2)) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO(2) series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d (SPM 24-ahead) = 0.906, d (CO 1-ahead) = 0.891, d (SO2 1-ahead) = 0.851).
    MeSH term(s) Air Pollution/analysis ; Carbon Monoxide/analysis ; Environmental Monitoring/methods ; Geography ; Models, Theoretical ; Particulate Matter/analysis ; Reproducibility of Results ; Spain ; Sulfur Dioxide/analysis
    Chemical Substances Particulate Matter ; Sulfur Dioxide (0UZA3422Q4) ; Carbon Monoxide (7U1EE4V452)
    Language English
    Publishing date 2007-10-11
    Publishing country Netherlands
    Document type Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 782621-7
    ISSN 1573-2959 ; 0167-6369
    ISSN (online) 1573-2959
    ISSN 0167-6369
    DOI 10.1007/s10661-007-9963-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 5186: Show issues

    Order via subito

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    Inter-library loan at ZB MED

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  6. Article: Prediction models of CO, SPM and SO₂ concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy

    Turias, Ignacio J / González, Francisco J / Martin, Ma. Luz / Galindo, Pedro L

    Environmental monitoring and assessment. 2008 Aug., v. 143, no. 1-3

    2008  

    Abstract: The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon ...

    Abstract The 'Campo de Gibraltar' region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO₂) and suspended particulate matter (SPM) series have been investigated (years 1999-2000-2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO₂ series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d SPM ₂₄₋ahead = 0.906, d CO ₁₋ahead = 0.891, d SO₂ ₁₋ahead = 0.851).
    Keywords air pollution ; neural networks
    Language English
    Dates of publication 2008-08
    Size p. 131-146.
    Publisher Springer Netherlands
    Publishing place Dordrecht
    Document type Article
    ZDB-ID 782621-7
    ISSN 1573-2959 ; 0167-6369
    ISSN (online) 1573-2959
    ISSN 0167-6369
    DOI 10.1007/s10661-007-9963-0
    Database NAL-Catalogue (AGRICOLA)

    Full text online

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    In stock of ZB MED Bonn / Germany

    Z 5186: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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