This use case focuses on enhancing manufacturing efficiency through IoT-to-Cloud computing by leveraging the CoGNETs solution. This use case aims to demonstrate how autonomous collaborative robots can transform production lines by improving real-time performance and facilitating data sharing within the AI economy.
By harnessing advanced AI technologies such as image recognition, anomaly detection, and computer vision, manufacturing industries can streamline operations and digitally transform their processes, allowing seamless interaction with existing equipment like handsets, robots, and IoT devices.
Enable manufacturing machines to autonomously manage resource exchanges and data access, optimising production line performance in real time.
Implement systems that preemptively detect and address potential faults, energy outages, and security breaches, ensuring smooth operations and reducing downtime.
Create new revenue streams by aligning service costs with factory uptime, allowing factories to pay only for managed services based on actual operational needs.
Pilot Use Case 2 (PUC2) focuses on enhancing the performance and efficiency of Battery Electric Vehicles (BEVs) through adaptive powertrain strategies, leveraging the CoGNETs solution to meet the EU’s carbon-neutral objectives by 2050.
With transport contributing nearly a quarter of Europe’s greenhouse gas emissions, achieving net zero targets poses significant challenges for the automotive industry. There is an urgent need to improve BEV performance while reducing energy consumption to support increasing mobility demands for both citizens and goods. Dynamic IoT-to-Cloud swarms will play a vital role in training algorithms that can overcome connectivity issues and enhance data privacy.
Implement collaborative machine learning-based control strategies to optimise vehicle performance by balancing energy availability, efficiency, and passenger comfort.
Establish a feedback loop between engineering teams and vehicle fleets to adjust powertrain operations based on customer usage and preferences, adapting to unforeseen driving cycles and events.
Utilise physics-based first principles models to ensure that the powertrain operates within safe conditions, preventing issues such as thermal runaways and safeguarding drivers and passengers.
Pilot Use Case 3 (PUC3) aims to leverage IoT-to-Cloud computing to transform the healthcare supply chain, enabling Health 4.0 applications and collaborative AI for medical data analytics that enhance care quality while reducing time and costs.
The integration of cognitive federated computing in the healthcare industry is essential for creating new, responsive, and accurate AI-enabled services. This approach allows healthcare providers to improve patient access and care quality while enabling professionals to focus on critical tasks rather than monotonous routines. Telcos and network operators can play a pivotal role by harnessing their credibility in developing data-driven healthcare services.
Evolve the analysis of extensive medical data sets to generate tailored insights for individual patients, turning previously untouched data into valuable analytics.
Enhance the security of medical data collection and analysis to provide healthcare professionals and patients with trustworthy diagnostic results, protecting against cyberattacks.
Develop AI-assisted smartphone applications for online health monitoring and diagnostics, supporting healthcare systems during crises such as the COVID-19 pandemic.
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