982 research outputs found
An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses
vities of transit buses traveling along arterial roads and city streets consist of frequent stops and idling events at many predictable occasions, e.g., loading/unloading passengers at bus stops, approaching traffic signals or stop signs, and going through recurrent traffic congestion, etc. Besides designing transit buses with electric powertrain systems that can save a noticeable amount of energy thanks to regenerative breaking, this urban traffic environment also unfolds a number of opportunities to further improve their energy efficiency via vehicle connectivity and autonomy. Therefore, this paper proposes a complete and novel simulation framework of integrated vehicle/powertrain eco-operation system for electric buses (Eco-bus) by co-optimizing the vehicle dynamics and powertrain (VD&PT) controls. A comprehensive evaluation of the proposed system on mobility benefits and energy savings has been conducted over various traffic conditions. Simulation results are presented to showcase the superiority of the proposed simulation framework of the Eco-bus compared to the conventional bus, particularly in terms of mobility and energy efficiency aspects
Soliton-potential interaction in the nonlinear Klein-Gordon model
The interaction of solitons with external potentials in nonlinear
Klein-Gordon field theory is investigated using an improved model. The
presented model has been constructed with a better approximation for adding the
potential to the Lagrangian through the metric of background space-time. The
results of the model are compared with another model and the differences are
discussed.Comment: 14 pages,8 figure
Inverse problem of photoelastic fringe mapping using neural networks
This paper presents an enhanced technique for inverse analysis of photoelastic fringes using neural networks to determine the applied load. The technique may be useful in whole-field analysis of photoelastic images obtained due to external loading, which may find application in a variety of specialized areas including robotics and biomedical engineering. The presented technique is easy to implement, does not require much computation and can cope well within slight experimental variations. The technique requires image acquisition, filtering and data extraction, which is then fed to the neural network to provide load as output. This technique can be efficiently implemented for determining the applied load in applications where repeated loading is one of the main considerations. The results presented in this paper demonstrate the novelty of this technique to solve the inverse problem from direct image data. It has been shown that the presented technique offers better result for the inverse photoelastic problems than previously published works
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Notwithstanding recent work which has demonstrated the potential of using
Twitter messages for content-specific data mining and analysis, the depth of
such analysis is inherently limited by the scarcity of data imposed by the 140
character tweet limit. In this paper we describe a novel approach for targeted
knowledge exploration which uses tweet content analysis as a preliminary step.
This step is used to bootstrap more sophisticated data collection from directly
related but much richer content sources. In particular we demonstrate that
valuable information can be collected by following URLs included in tweets. We
automatically extract content from the corresponding web pages and treating
each web page as a document linked to the original tweet show how a temporal
topic model based on a hierarchical Dirichlet process can be used to track the
evolution of a complex topic structure of a Twitter community. Using
autism-related tweets we demonstrate that our method is capable of capturing a
much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 201
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