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EcoMobility: CT develops predictive digital twin for road traffic in smart cities.
Using machine learning technology, the CT team trains decision-making models for vehicles and people, allowing the digital twin to autonomously make decisions in real time, as if it were a human mind.
Read moreCT has completed the IADGENOL project on automating AWE System trajectories using deep learning.
After two years of research, CT has successfully developed a Deep Learning-based control model that addresses the dynamic challenges of autonomous AWE system operations. In collaboration with Carlos III University of Madrid, CT showcased the results of the latest validation tests in a simulation environment for the AWES control system at the European AWES Congress, AWEC2024, in Madrid, demonstrating precise wind alignment and optimal energy generation trajectories.
Read moreCT will present 5.0 solutions for collaborative naval defense missions at Navalia 2024.
CT will be back at Navalia, this time with two distinct spaces, to showcase its catalogue of digital solutions, as well as its role as an integrator of 5.0 technologies with interoperability software solutions for collaborative missions in the defense sector. Among its digital solutions, visitors will have the chance to discover more about life cycle management and integrated logistics support, or about power electronics products in the naval sector.
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