Browsing by Keyword "Data fusion"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
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 Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0(2019-10) Diez-Olivan, Alberto; Del Ser, Javier; Galar, Diego; Sierra, Basilio; Tecnalia Research & Innovation; IAThe so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.Item Enhanced sensing error probability estimation for iterative data fusion in the low SNR regime(2010) Olabarrieta, Ignacio; Del Ser, Javier; IAIn this paper we consider a network of distributed sensors which simultaneously measure a physical parameter of interest, subject to a certain probability of sensing error. The sensed information at each of such nodes is channel-encoded and forwarded to a central receiver through parallel independent AWGN channels. In this scenario, several recent contributions have shown that the end-to-end Bit Error Rate (BER) performance can be dramatically improved if the decoders associated to each received signal and the data fusion stage exchange soft information in an iterative Turbo-like fashion. In order to achieve optimum performance, the probability of sensing error must be known (or estimated) at the receiver. In this work we describe a novel method for estimating such sensing error probability by properly weighting likelihoods output from the Soft-Input Soft-Output decoders (SISO), which is shown to outperform other estimation methods based in hard-decision comparisons, specially in the low SNR regime.Item Environmental perception for intelligent vehicles(Elsevier, 2017-01-01) Armingol, José M.; Alfonso, Jorge; Aliane, Nourdine; Clavijo, Miguel; Campos-Cordobés, Sergio; de la Escalera, Arturo; del Ser, Javier; Fernández, Javier; García, Fernando; Jiménez, Felipe; López, Antonio M.; Mata, Mario; Martín, David; Menéndez, José M.; Sánchez-Cubillo, Javier; Vázquez, David; Villalonga, Gabriel; LABORATORIO DE TRANSFORMACIÓN URBANA; SMART_TRANSPORT; Tecnalia Research & InnovationEnvironmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems.