15,167 research outputs found
Charging ahead on the transition to electric vehicles with standard 120 v wall outlets
Electrification of transportation is needed soon and at significant scale to meet climate goals, but electric vehicle adoption has been slow and there has been little systematic analysis to show that today's electric vehicles meet the needs of drivers. We apply detailed physics-based models of electric vehicles with data on how drivers use their cars on a daily basis. We show that the energy storage limits of today's electric vehicles are outweighed by their high efficiency and the fact that driving in the United States seldom exceeds 100 km of daily travel. When accounting for these factors, we show that the normal daily travel of 85-89% of drivers in the United States can be satisfied with electric vehicles charging with standard 120 V wall outlets at home only. Further, we show that 77-79% of drivers on their normal daily driving will have over 60 km of buffer range for unexpected trips. We quantify the sensitivities to terrain, high ancillary power draw, and battery degradation and show that an extreme case with all trips on a 3% uphill grade still shows the daily travel of 70% of drivers being satisfied with electric vehicles. These findings show that today's electric vehicles can satisfy the daily driving needs of a significant majority of drivers using only 120 V wall outlets that are already the standard across the United States
Learning to Represent Haptic Feedback for Partially-Observable Tasks
The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can be extremely challenging. In this work, we consider manipulation
tasks that need to incorporate tactile sensor feedback in order to modify a
provided nominal plan. To incorporate partial observation, we present a new
framework that models the task as a partially observable Markov decision
process (POMDP) and learns an appropriate representation of haptic feedback
which can serve as the state for a POMDP model. The model, that is parametrized
by deep recurrent neural networks, utilizes variational Bayes methods to
optimize the approximate posterior. Finally, we build on deep Q-learning to be
able to select the optimal action in each state without access to a simulator.
We test our model on a PR2 robot for multiple tasks of turning a knob until it
clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201
Space-time fractional reaction-diffusion equations associated with a generalized Riemann-Liouville fractional derivative
This paper deals with the investigation of the computational solutions of an
unified fractional reaction-diffusion equation, which is obtained from the
standard diffusion equation by replacing the time derivative of first order by
the generalized Riemann-Liouville fractional derivative defined in Hilfer et
al. , and the space derivative of second order by the Riesz-Feller fractional
derivative, and adding a function . The solution is derived by the
application of the Laplace and Fourier transforms in a compact and closed form
in terms of Mittag-Leffler functions. The main result obtained in this paper
provides an elegant extension of the fundamental solution for the space-time
fractional diffusion equation obtained earlier by Mainardi et al., and the
result very recently given by Tomovski et al.. At the end, extensions of the
derived results, associated with a finite number of Riesz-Feller space
fractional derivatives, are also investigated.Comment: 15 pages, LaTe
Boltzmann-Gibbs Entropy Versus Tsallis Entropy: Recent Contributions to Resolving the Argument of Einstein Concerning "Neither Herr Boltzmann nor Herr Planck has given a definition of W"?
Classical statistical mechanics of macroscopic systems in equilibrium is
based on Boltzmann's principle. Tsallis has proposed a generalization of
Boltzmann-Gibbs statistics. Its relation to dynamics and nonextensivity of
statistical systems are matters of intense investigation and debate. This essay
review has been prepared at the occasion of awarding the 'Mexico Prize for
Science and Technology 2003'to Professor Constantino Tsallis from the Brazilian
Center for Research in Physics.Comment: 5 pages, LaTe
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