BNL, Upton, New York, USA
The electronic logbook (elog) system used at Brookhaven National Laboratory’s Collider-Accelerator Department (C-AD) allows users to customize logbook settings, including specification of favorite logbooks. Using machine learning techniques, customizations can be further personalized to provide users with a view of entries that match their specific interests. We will utilize natural language processing (NLP), optical character recognition (OCR), and topic models to augment the elog system. NLP techniques will be used to process and classify text entries. To analyze entries including images with text, such as screenshots of controls system applications, we will apply OCR. Topic models will generate entry recommendations that will be compared to previously tested language processing models. We will develop a command line interface tool to ease automation of NLP tasks in the controls system and create a web interface to test entry recommendations. This technique will create recommendations for each user, providing custom sets of entries and possibly eliminate the need for manual searching.
