Papers by Luciano Sánchez
14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021)
Engineering Applications of Artificial Intelligence
Aqua-LAC
Las pequeñas localidades del noroeste de la Provincia de Santa Fe son conocedoras de los problema... more Las pequeñas localidades del noroeste de la Provincia de Santa Fe son conocedoras de los problemas de escasez de agua en cantidad, calidad y oportunidad. Un régimen de precipitaciones irregular, distancias largas a cursos de agua superficial, aguas subterráneas con elevada salinidad y/o altos contenidos de arsénico, plantean limitantes para el abastecimiento humano, rural e industrial. Diversas soluciones se aproximan para garantizar el consumo de agua segura. Sin embargo, la falta de involucramiento de los usuarios y destinatarios de los proyectos de agua y/o saneamiento desde el inicio de la gestión, limitaciones o carencias de conocimiento, asimetrías de información, inexistencia o no aplicación de mecanismos de participación y ausencia de compromiso institucional local, entre otros, suelen conducir al fracaso de las tecnologías que se diseñan para suplir los requerimientos sanitarios por la no apropiación o adopción social de las mismas. En este trabajo se presentan algunos line...
Computers in Biology and Medicine
Sensors
An iterative algorithm is proposed for determining the optimal chassis design of an electric vehi... more An iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb com...
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Many real-world classification systems must comply with a series of inherent restrictions to the ... more Many real-world classification systems must comply with a series of inherent restrictions to the problem at hand such as response times, power consumptions or computational costs. This poses a fundamental limitation to traditional performance-driven classifiers and learning algorithms by restraining their applicability in cost-sensitive scenarios. Because of this, fuzzy systems are leveraged to learn cost-conscious multi-stage classifiers through multiobjective optimization to find a set of optimal tradeoffs between accuracy and any related cost. This approach allows find a suitable balance between all objectives regardless of the scenario. Experimental evaluations were done for Sound Environment Classification in modern battery-powered hearing aids by jointly optimising classification accuracy and computational costs.
International Journal of Approximate Reasoning
A framework is proposed for learning fuzzy rule-based systems from low quality data where the dif... more A framework is proposed for learning fuzzy rule-based systems from low quality data where the differences between observed and true values may introduce systematic bias in the model. It is argued that there are problems where aggregating imprecise losses into numerical or fuzzy-valued risk functions discards useful information, thus generalizing the risk of a model to a vector of fuzzy losses is preferred. The principles governing a learner that is capable of optimizing these fuzzy multivariate risk functions are discussed. Illustrative use cases are worked to exemplify those situations where new framework could become the alternative of choice.
Journal of Sensors
A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physica... more A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.
Sensors (Basel, Switzerland), Jan 21, 2017
A soft sensor is presented that approximates certain health parameters of automotive rechargeable... more A soft sensor is presented that approximates certain health parameters of automotive rechargeable batteries from on-vehicle measurements of current and voltage. The sensor is based on a model of the open circuit voltage curve. This last model is implemented through monotonic neural networks and estimate over-potentials arising from the evolution in time of the Lithium concentration in the electrodes of the battery. The proposed soft sensor is able to exploit the information contained in operational records of the vehicle better than the alternatives, this being particularly true when the charge or discharge currents are between moderate and high. The accuracy of the neural model has been compared to different alternatives, including data-driven statistical models, first principle-based models, fuzzy observers and other recurrent neural networks with different topologies. It is concluded that monotonic echo state networks can outperform well established first-principle models. The al...
Engineering Applications of Artificial Intelligence
Lecture Notes in Computer Science, 2016
Lecture Notes in Computer Science, 2016
Engineering Applications of Artificial Intelligence, 2016
Energies, 2016
As data and supercomputing centres increase their performance to improve service quality and targ... more As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Also, high power consumptions are building up to a remarkable bottleneck for the expansion of these infrastructures in economic terms due to the unavailability of sufficient energy sources. A substantial part of the problem is caused by current energy consumptions of High Performance Computing (HPC) clusters. To alleviate this situation, we present in this work EECluster, a tool that integrates with multiple open-source Resource Management Systems to significantly reduce the carbon footprint of clusters by improving their energy efficiency. EECluster implements a dynamic power management mechanism based on Computational Intelligence techniques by learning a set of rules through multi-criteria evolutionary algorithms. This approach enables cluster operators to find the optimal balance between a reduction in the cluster energy consumptions, service quality, and number of reconfigurations. Experimental studies using both synthetic and actual workloads from a real world cluster support the adoption of this tool to reduce the carbon footprint of HPC clusters.
Information Sciences, 2016
Studies in Fuzziness and Soft Computing, 2003
Advances in Intelligent Systems and Computing, 2015
Mathematical Modelling: Theory and Applications, 2008
In control engineering, it is well known that many physical processes exhibit a chaotic component... more In control engineering, it is well known that many physical processes exhibit a chaotic component. In point of fact, it is also assumed that conventional modeling procedures disregard it, as stochastic noise, beside nonlinear universal approximators (like neural networks, fuzzy rule-based or genetic programming-based models,) can capture the chaotic nature of the process. In this chapter we will show that this is not always true. Despite the nonlinear capabilities of the universal approximators, these methods optimize the one step prediction of the model. This is not the most adequate objective function for a chaotic model, because there may exist many different nonchaotic processes that have near zero prediction error for such an horizon. The learning process will surely converge to one of them. Unless we include in the objective function some terms that depend on the properties on the reconstructed attractor, we may end up with a non chaotic model. Therefore, we propose to follow a multiobjective approach to model chaotic processes, and we also detail how to apply either genetic algorithms or simulated annealing to obtain a difference equations-based model.
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Papers by Luciano Sánchez