This work provides a comprehensive review of data preprocessing and machine learning approaches applied to estimate a battery's state of charge (SOC) and state of health (SOH) over the past five years. The standard procedure for preprocessing battery time series data and the associated techniques to address inherent challenges are described. Dominant machine learning architectures and their applications in SOC and SOH estimation are explored. Additionally, potential directions for future research are highlighted.
Closed the acquisition of Pangiam in an all-stock transaction, combining BigBear.ai’s computer vision capabilities with facial recognition, image-based anomaly detection and advanced.
BigBear.ai today announced that it has been designated as an “Awardable” vendor for the Chief Digital and Artificial Intelligence Office’s Tradewinds Solutions Marketplace. Five of the.
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This study identifies the feasibility of leveraging deep learning anomaly detection to identify event-driven traveling ionospheric disturbances (TIDs). DR. JIHYE PARK, FIONA LUHRMANN, DR. WENG-KEEN WONG, OREGON STATE UNIVERSITY Ionospheric responses to various geophysical events have been studi