MIT researchers have developed an algorithm that accurately aligns partial trajectories in real-time, allowing motion predictors to accurately anticipate the timing of a person’s motion to make human-robot interaction safer.
Extract, transform, load (ETL) software can help improve data gathering for operations technology (OT) applications, but there are major challenges with data integration that companies need to overcome.
Articles about the Career and Salary Survey, CNC digitalization, alarm management and process safety, process instrumentation education, and asset performance in refineries were Control Engineering’s five most clicked articles from June 3-9. Miss something? You can catch up here.
Ypsomed, a medical technology company, was able to retrofit Industrie 4.0 digital control systems onto its legacy plastic injection molding machines.
Machine learning and other advanced technologies are embedded in advanced analytics software to empower engineers and other experts.
North Carolina State University researchers have developed AOGNets, a framework for building deep neural networks that uses a compositional grammar approach to extract useful information from raw data.
Making the connection between alarm management and process safety management can ensure a safe and productive process.
MIT researchers have developed an autonomous control system that “learns” the steering patterns of human drivers as they navigate roads in a small area, using only data from video camera feeds and a simple GPS-like map. See video.
The MIT-Air Force AI Accelerator, will conduct fundamental research directed at rapid deployment of artificial intelligence (AI) innovations in operations, data management, cybersecurity, and vehicle safety.
Getting the most out of an investment in manufacturing IT seldom produces the desired results unless a simultaneous review and redesign of business processes is undertaken.