AIM is one of the leading Autonomous Intersection Management mechanisms based on Multiagent System (MAS) for alleviating traffic congestion specially at intersections. One of the concerned problems on AIM, however, lies in the communication complexity of the system. Previously, the driver agent has no choice, but to completely retransmit its adjusted request information when the former reservation is rejected by the intersection manager, which results in the increase of interaction complexity between agents and the plenty of redundant data transmission. In this paper, we present an incremental data synchronization policy ksync for driver agent to avoid such redundant retransmission. In particular, we first introduce the basic properties of ksync policy. Second, we demonstrate how ksync could be well integrated into the knowledge base of driver agent as one of its essential policies. Third, we prove by experimental evaluation that the average data compression rate can be improved by over 80% exploiting ksync. Finally, we propose some of the most significant research prospects on ksync using the techniques in data mining and machine learning.
In this paper, we propose a novel data-driven method that uses a machine learning scheme for formulating fracture simulation with the boundary element method (BEM) as a regression problem. With this method, the crack opening displacement (COD) of every correlation node is predicted at the next frame.In our naive prediction, we design a feature vector directly exploiting stress intensities and toughness at the current frame so that our method predicts the COD at the next frame more reliably. Thus, there is no need to solve the original linear BEM system to calculate displacements. This enables us to propagate crack fronts using the estimated stress intensities. There are existing works that use the machine learning approach to accelerate the speed of traditional physics-based simulations like smoke and fluid, but our work is the first to incorporate the machine learning scheme into BEM-based fracture simulations. Our implementation accelerates the acquisition of displacements in linear time over the number of crack fronts at each time step compared with the conventional solution whose time complexity grows exponentially based on the BEM linear system. The databases generated by our method are versatile and can be applied to general situations and different models. KEYWORDS boundary element method, brittle fracture, data-driven, regression forest Comput Anim Virtual Worlds. 2019;30:e1865.wileyonlinelibrary.com/journal/cav is the graduate student from the Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Japan. His research interests include computer animation and machine learning.Yonghang Yu received her master's degree from the Graduate School of Arts and Sciences, The University of Tokyo, Japan. Her research interests include computer animation and machine learning. She currently works as a researcher at Y-Lab Beijing Kwai Technology Co., Ltd.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.