Situ Sensing

In-situ sensing focuses on real-time monitoring of processes and systems to enable immediate feedback and control, improving efficiency and quality. Current research emphasizes the application of deep learning, particularly generative models and LSTM networks, for data analysis and prediction, often coupled with advanced feature extraction techniques to address data volume and privacy concerns. This approach is proving valuable in diverse fields, from additive manufacturing defect detection and process optimization to autonomous control of continuous processes and anomaly detection in smart buildings, ultimately leading to improved automation and enhanced system performance.