Zerrendatu honen arabera: gaia "Machine learning"
17-tik 1-17 emaitza erakusten
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Analyzing Inter-Vehicle Collision Predictions during Emergency Braking with Automated Vehicles
(IEEE, 2023)Automated Vehicles (AVs) require sensing and perception to integrate data from multiple sources, such as cameras, lidars, and radars, to operate safely and efficiently. Collaborative sensing through wireless vehicular ... -
Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
(IEEE, 2020)The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ... -
Automatic Web Navigation Problem Detection Based on Client-Side Interaction Data
(Korea Information Processing Society-Computer Software Research Group, 2021)The current importance of digital competence makes it essential to enable people with disabilities to use digital devices and applications and to automatically adapt site interactions to their needs. Although most of the ... -
Big Data Life Cycle in Shop-floor. Trends and Challenges
(IEEE, 2023)Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from ... -
The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
(Springer Verlag, 2019)Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, ... -
Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks
(Elsevier, 2023)There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being ... -
Data-driven energy resource planning for Smart Cities
(IEEE, 2020)Cities are growing and, therefore, the primary needs, such as the energy resources. Hence, managing them in the proper way becomes essential for a sustainable growth. This paper proposes a data-driven tool based on IoT ... -
Data-Driven Fault Diagnosis for Electric Drives: A Review
(MDPI, 2021)The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has ... -
A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems
(Elsevier, 2022)The potential of Marine Renewable Energy (MRE) systems is usually evaluated based on recent metocean data and assuming the stationarity of the MRE resource. Yet, different studies in the literature have shown long-term ... -
Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics
(Elsevier, 2023)In the transition towards more sustainable ships, electric motors (EM) are being used in ship propulsion systems to reduce emissions and increase efficiency. The safe operation of ships is crucial, and prognostics and ... -
Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery
(IEEE, 2020)This paper presents the implementation and explanations of a remaining life estimator model based on machine learning, applied to industrial data. Concretely, the model has been applied to a bushings testbed, where fatigue ... -
A maturity model for the autonomy of manufacturing systems
(Springer, 2023)Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require ... -
A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines
(Elsevier, 2023)In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based ... -
Probabilistic feature selection for improved asset lifetime estimation in renewables. Application to transformers in photovoltaic power plants
(Elsevier, 2024)The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation ... -
Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation
(Taylor & Francis, 2021)Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can ... -
Survey on decentralized congestion control methods for vehicular communication
(Elsevier Ltd., 2022)Vehicular communications have grown in interest over the years and are nowadays recognized as a pillar for the Intelligent Transportation Systems (ITSs) in order to ensure an efficient management of the road traffic and ... -
Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms
(IEEE, 2021)The software of elevators requires maintenance over several years to deal with new functionality, correction of bugs or legislation changes. To automatically validate this software, test oracles are necessary. A typical ...
