Static tire properties analysis and static parameters derivation to characterising tire model using experimental and numerical solutions

Tire-ground interaction plays substantial role in determining vehicle kinetics and kinematics yet a clear understanding of such an interaction outputs is complex process. The present study aims at static analysis of tire parameters using both experimental and numerical based finite element method (FEM) solutions. To this end, tire cross section shape with different inflation pressures, vertical stiffness together with the footprint were measured using controlled apparatus and then are compared with the simulation results in order that the accuracy of the FE tire model in static condition can be validated. The 3D tire model was obtained by revolving the 2D axisymmetric tire model, and static stiffness and footprint were predicted using the 3D model. Inflation pressure analysis was presented by comparing the tire cross-section shape variation at different inflation pressures. The conclusions will serve future investigations as a concise knowledge source to develop improved tire models

A Totally Astar-based Multi-path Algorithm for the Recognition of Reasonable Route Sets in Vehicle Navigation Systems

AbstractCompared with a Dijkstra-based or partially Astar-based one, a totally Astar-based algorithm is proposed in the paper for vehicle navigation systems. It has a better performance such as computing speed and veracity in a large-scale road network than a Dijkstra-based one because the computational complexity of Astar algorithm has little connection with the overall scale of a road network. To recognize all the reasonable routes between a specific OD pair, this algorithm takes all the geometrically reasonable routes into account and considers several constraints that meet the drivers’ preferences like circuitous route, the number of turns and traffic control strategy (for example, no left turn). Two numerical examples demonstrate the operation and efficiency of the algorithm

On the Depth of Deep Neural Networks: A Theoretical View

People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.Comment: AAAI 201