Browsing by Keyword "Neural networks"
Now showing 1 - 10 of 10
Results Per Page
Sort Options
Item Award Price Estimator for Public Procurement Auctions Using Machine Learning Algorithms: Case Study with Tenders from Spain(2021) Rodriguez, Manuel J.Garcia; Montequin, Vicente Rodriguez; Aranguren Ubierna, Andoni; Hermida, Roberto Santana; Araujo, Basilio Sierra; Jauregi, Ana Zelaia; IAThe public procurement process plays an important role in the efficient use of public resources. In this context, the evaluation of machine learning techniques that are able to predict the award price is a relevant research topic. In this paper, the suitability of a representative set of machine learning algorithms is evaluated for this problem. The traditional regression methods, such as linear regression and random forest, are compared with the less investigated paradigms, such as isotonic regression and popular artificial neural network models. Extensive experiments are conducted based on the Spanish public procurement announcements (tenders) dataset and employ diverse error metrics and implementations in WEKA and Tensorflow 2.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 Decoding of individuated finger movements using surface electromyography(2009-05) Tenore, Francesco V.G.; Ramos, Ander; Fahmy, Amir; Acharya, Soumyadipta; Etienne-Cummings, Ralph; Thakor, Nitish V.; Medical TechnologiesUpper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals.While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movementswould require more elaborate control schemes.We showthat it is possible to decode individual flexion and extension movements of each finger (tenmovements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference (p<0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.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 Embedded system used for classifying motor activities of elderly and disabled people(2009-08) Fourty, Nicolas; Guiraud, D.; Fraisse, P.; Perolle, G.; Etxeberria, I.; Val, T.; SGOur modern societies are confronted to a new growing problem: the global ageing of population. In order to find ways to encourage elderly people to live longer in their own home, ensuring the necessary vigilance and security at the lowest cost, some tele-assistance systems are already available commercially. This paper presents an embedded prototype able to detect automatically the falls of elderly people while monitoring their motor activities. The classification algorithm using an artificial neural network, the communication and location capabilities of this system are specifically highlighted. In the last part, some experimental results and social issues stemming from Gerontologic Institute Ingema are discussed.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 A hybrid approach to the development of a multilayer neural network for wear and fatigue prediction in metal forming(2007-10) Belfiore, N. P.; Ianniello, F.; Stocchi, D.; Casadei, F.; Bazzoni, D.; Finzi, A.; Carrara, S.; González, J. R.; Llanos, J. M.; Heikkila, I.; Peñalba, F.; Gómez, X.; EXTREMATIn this paper an approach to surface damage prediction is proposed for the case of metal forming. The method is mainly based on three fundamental stages: (a) the detection of a feasible physical model which is able to give some important understanding of the phenomenon, although with limited generality; (b) the extensive development of an organized experimental campaign, which is necessary to tune up the developed model; and (c) the organization of an efficient and intelligent way of data collecting. The three aspects of the research work have been integrated by means of a neural network which is trained by using data coming from the real plant, from the standard tribometers, and from the reference numerical model. In this sense, the neural network is indented as hybridized. Predictions are shown to be very close to the experimental data obtained in the production plant. The method is useful for minimizing the number of experiments in the process of materials and treatment selection, and in maintenance.Item Joint feature selection and parameter tuning for short-term traffic flow forecasting based on heuristically optimized multi-layer neural networks(Springer Verlag, 2017) Laña, Ibai; Del Ser, Javier; Vélez, Manuel; Oregi, Izaskun; Del Ser, Javier; IA; QuantumShort-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on manual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Results are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuristic wrapper outperform those tuned by means of na¨ıve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction.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.Item Seventh international workshop on reverse variability engineering (REVE 2019)(Association for Computing Machinery, 2019-09-09) Acher, Mathieu; Ziadi, Tewfik; Lopez-Herrejon, Roberto E.; Martinez, Jabier; Berger, Thorsten; Collet, Philippe; Duchien, Laurence; Fogdal, Thomas; Heymans, Patrick; Kehrer, Timo; Martinez, Jabier; Mazo, Raul; Montalvillo, Leticia; Salinesi, Camille; Ternava, Xhevahire; Thum, Thomas; Ziadi, Tewfik; SWTSoftware Product Line (SPL) migration remains a challenging endeavour. From organizational issues to purely technical challenges, there is a wide range of barriers that complicates SPL adoption. This workshop aims to foster research about making the most of the two main inputs for SPL migration: 1) domain knowledge and 2) legacy assets. Domain knowledge, usually implicit and spread across an organization, is key to define the SPL scope and to validate the variability model and its semantics. At the technical level, domain expertise is also needed to create or extract the reusable software components. Legacy assets can be, for instance, similar product variants (e.g., requirements, models, source code etc.) that were implemented using ad-hoc reuse techniques such as clone-and-own. More generally, the workshop REverse Variability Engineering (REVE) attracts researchers and practitioners contributing to processes, techniques, tools, or empirical studies related to the automatic, semi-automatic or manual extraction or refinement of SPL assets.