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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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation
(Elsevier, 2021)
In this manuscript we report on a vision-based data-driven methodology for industrial robot health assessment. We provide an experimental evidence of the usefulness of our methodology on a system comprised of a 6-axis ...