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Communities in RUIdeRA
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A new model to predict the tool life in turning of titanium aluminides.
(Springer, 2023) García Martínez, Enrique; Miguel Eguia, Valentín; Martínez Martínez, Alberto; Ayllón Pérez, Jorge
Several tool wear models for machining operations have been reported in the literature. However, most of them are capable of modeling only a part of the wear curve, designed for quasi-linear curves or need to be fitted by means of differential equations. In this research, a new analytical easy-to-use wear model is proposed, taking into consideration the geometry of the cutting insert and the influence of the cutting speed and the tool-workpiece contact stress. In addition, the effect of the critical machining time, relative to the tool degradation, has also been introduced. The wear model describes completely the wear curve, from the initial rapid wear stage to the thermal-activated wear rate acceleration. The constants and parameters of the equation are determined by cutting experiments from the cutting forces and flank wear registers under a variety of technological parameters. This model has been validated for turning process on Ti48Al2Cr2Nb aluminide with uncoated carbide tool under different cutting conditions, varying the cutting speed and the geometrical position of the tool. It has been demonstrated its suitability to predict tool life, according to a defined wear criterion.
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Deep learning assisted cognitive diagnosis for the D-Riska application.
(Springer, 2022) Cuerda,Cristian; Zornoza Martínez, Alejandro José; Gallud Lázaro, Jose Antonio; Tesoriero Pszytula, Ricardo; Romero Ayuso, Dulce
In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics.
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Shape binary patterns: an efficient local descriptor and keypoint detector for point clouds
(Springer, 2022) Romero González, Cristina; García Varea, Ismael; Martínez Gómez, Jesus
Many of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors.
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Evaluating cloud interactions with costs and SLAs
(Springer, 2022) Bernal Bermejo, Adrián; Cambronero Piqueras, María Emilia; Núñez, Alberto; Cañizares, Pablo C.; Valero Ruiz, Valentín
In this paper, we investigate how to improve the profits in cloud infrastructures by using price schemes and analyzing the user interactions with the cloud provider. For this purpose, we consider two different types of client behavior, namely regular and high-priority users. Regular users do not require a continuous service, and they can wait to be attended to. In contrast, high-priority users require a continuous service, e.g., a 24/7 service, and usually need an immediate answer to any request. A complete framework has been implemented, which includes a UML profile that allows us to define specific cloud scenarios and the automatic transformations to produce the code for the cloud simulations in the Simcan2Cloud simulator. The engine of Simcan2Cloud has also been modified by adding specific SLAs and price schemes. Finally, we present a thorough experimental study to analyze the performance results obtained from the simulations, thus making it possible to draw conclusions about how to improve the cloud profit for the cloud studied by adjusting the different parameters and resource configuration.
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A comparison of different soft-computing techniques for the evaluation of handball goalkeepers.
(Springer, 2022) Angulo Sánchez-Herrera, Eusebio; Romero Chicharro, Francisco Pascual; López Gómez, Julio Alberto
The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computingmethodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledgebased methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men’s Handball Championship, in order to estimate the weights of the indicators.