ML Framework Cuts Industrial System Design Time By 60%
InfoPos cuts industrial design time by 60% & boosts reliability with data-centric approach & automated anomaly detection. A smart assistant for engineers building better control systems.
This is a Plain English Papers summary of a research paper called ML Framework Cuts Industrial System Design Time by 60% While Boosting Reliability. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter. Overview • Machine learning framework called InfoPos to help design industrial cyber-physical systems • Uses data-centric approach to identify anomalies and system issues • Focuses on information positioning to improve system reliability • Enables automated anomaly detection and solution design support • Funded by Dutch Research Council under ZORRO...