PCA and K-means clustering applied to Raman and PL imaging reveal structural defects in silicon wafers, enhancing understanding of optoelectronic performance.

Unlike PCA (maximum variance) or ICA (maximum independence), ForeCA finds components that are maximally forecastable. This makes it ideal for time series analysis where prediction is often the primary ...

Abstract: This work studies the use of RPCA in IoT sensor data for anomaly detection. However, due to the fact that IoT data is complex, high-dimensional, noisy, and frequently has a dynamic pattern, ...

AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...

Abstract: In semiconductor manufacturing, virtual metrology (VM) leverages high-dimensional sensor data for real-time quality estimation. However, excessive sensor deployment leads to increased ...