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10.1007/978-3-030-51758-8_8 http://scihub22266oqcxt.onion/10.1007/978-3-030-51758-8_8 C7325289!7325289!C7325289     free     free     free       Advances+in+Human+Factors+in+Robots,+Drones+and+Unmanned+Systems 2021 ; 1210 (ä): 56-63 Nephropedia Template TP {{tp|p=C7325289|t=2021. Learn on the Fly |pdf=|usr=}}{{C7325289}} gab.com Text Learn on the Fly JO: Advances in Human Factors in Robots, Drones and Unmanned Systems http://www.kidney.de/pget.php?&tw=1&pmid=C7325289 #FOAvip In this study, we explore the biologically-inspired Learn-On-The-Fly (LOTF) method that actively learns and discovers patterns with improvisation and sensory intelligence, including pheromone trails, structure from motion, sensory fusion, sensory inhibition, and spontaneous alternation. LOTF is related to classic online modeling and adaptive modeling methods. However, it aims to solve more comprehensive, ill-structured problems such as human activity recognition from a drone video in a disastrous environment. It helps to build explainable AI models that enable human-machine teaming with visual representation, visual reasoning, and machine vision. It is anticipated that LOTF would have an impact on Artificial Intelligence, video analytics for searching and tracking survivors? activities for humanitarian assistance and disaster relief (HADR), field augmented reality, and field robotic swarms. Twit Text FOAVip Learn on the Fly ????Advances in Human Factors in Robots, Drones and Unmanned Systems http://www.kidney.de/pget.php?&tw=1&pmid=C7325289 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7325289 #FOAvip English Wikipedia <ref>{{Cite pmid|C7325289}}</ref> Learn on the Fly #MMPMIDC7325289 Cai Y Advances in Human Factors in Robots, Drones and Unmanned Systems 2021[]; 1210 (ä): 56-63 PMIDC7325289show ga In this study, we explore the biologically-inspired Learn-On-The-Fly (LOTF) method that actively learns and discovers patterns with improvisation and sensory intelligence, including pheromone trails, structure from motion, sensory fusion, sensory inhibition, and spontaneous alternation. LOTF is related to classic online modeling and adaptive modeling methods. However, it aims to solve more comprehensive, ill-structured problems such as human activity recognition from a drone video in a disastrous environment. It helps to build explainable AI models that enable human-machine teaming with visual representation, visual reasoning, and machine vision. It is anticipated that LOTF would have an impact on Artificial Intelligence, video analytics for searching and tracking survivors? activities for humanitarian assistance and disaster relief (HADR), field augmented reality, and field robotic swarms. äLearn on the Fly (epmc).pdf DeepDyve Pubget Overpricing