Browsing by Keyword "Neural networks"
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Item Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions: state of the art and future directions(2021-08-03) Torre-Bastida, Ana I.; Díaz-de-Arcaya, Josu; Osaba, Eneko; Muhammad, Khan; Camacho, David; Del Ser, Javier; HPA; QuantumThis overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.Item Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles(2019-02) Dendaluce Jahnke, Martin; Cosco, Francesco; Novickis, Rihards; Pérez Rastelli, Joshué; Gomez-Garay, Vicente; Tecnalia Research & Innovation; CCAMThe combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Item An energy efficient intelligent torque vectoring approach based on fuzzy logic controller and neural network tire forces estimator(2021-01-13) Parra, Alberto; Zubizarreta, Asier; Pérez, Joshué; Tecnalia Research & Innovation; CCAMIn electric vehicles (EVs) with multiple motors, torque vectoring (TV) control can effectively enhance the cornering response and safety. Moreover, TV systems can also improve the overall efficiency through an optimal torque distribution that also considers the power consumption. For such a complex control system with multiple objectives, intelligent control techniques have demonstrated to be one of the best alternatives. However, the works proposed in the literature do not handle both vehicle dynamics behavior and energy efficiency, and generally do not consider the real-time implementability of the developed controllers. To overcome the aforementioned isues, in this work, a novel torque vectoring approach is proposed, which uses a neural network-based vertical tire forces estimator and considers the regenerative braking capabilities of EVs. Moreover, the implementability of the controller in a heterogenous (FPGA and microcontroller) automotive suitable system on chip is addressed, ensuring its real-time capabilities. For the sake of validating the proposed approach, a set of experiments have been carried out in a hardware in the loop setup. The performance of the proposed TV approach has been compared with other two TV approaches from the literature, evaluating them in several challenging manoeuvres in high and low tire-road friction coefficient scenarios. Results show that the proposed approach not only is able to enhance the vehicle dynamics behavior but also to decrease the energy consumption about 13%.Item Persistence in complex systems(2022-04-29) Salcedo-Sanz, S.; Casillas-Pérez, D.; Del Ser, J.; Casanova-Mateo, C.; Cuadra, L.; Piles, M.; Camps-Valls, G.; IAPersistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.