Fundamental String (Membrane) Orbiting D5(M5)-branes

We study fundamental string (F-string) dynamics near D5-brane in some limits. We find that when the angular momentum of the probe is proportional to string length (l_s) and square root of string coupling constant {g_s}, the F-string lies in its metastable orbit at a finite distance from D5-branes. We further study the metastable orbits of a M2-brane in M5-brane background.Comment: 13 pages, In JHEP Styl

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

Superconducting Bearing Design for Outer Rotor Flywheel Using Lumped Parameter Techniques

This paper describes the application of lumped parameter modeling techniques to designing high temperature superconducting bearings for outer-rotor flywheel energy storage systems. The lumped parameter models decrease computational time by 99% compared to Finite Element Analysis (FEM) without compromising fidelity needed to capture the non-linear and hysteretic force-displacement behavior between a levitated permanent magnet and bulk superconductor. The techniques formulated can be used to quickly evaluate lifting capacity and translational stiffness for a superconducting bearing design. The validity of the modeling approach has been verified by comparing results from FEM studies and experimental tests.Center for Electromechanic

Uncertainty Quantification in Three Dimensional Natural Convection using Polynomial Chaos Expansion and Deep Neural Networks

This paper analyzes the effects of input uncertainties on the outputs of a three dimensional natural convection problem in a differentially heated cubical enclosure. Two different cases are considered for parameter uncertainty propagation and global sensitivity analysis. In case A, stochastic variation is introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers) with an assumption that the boundary temperature is uniform. Being a two dimensional stochastic problem, the polynomial chaos expansion (PCE) method is used as a surrogate model. Case B deals with non-uniform stochasticity in the boundary temperature. Instead of the traditional Gaussian process model with the Karhunen-Lo$\grave{e}$ve expansion, a novel approach is successfully implemented to model uncertainty in the boundary condition. The boundary is divided into multiple domains and the temperature imposed on each domain is assumed to be an independent and identically distributed (i.i.d) random variable. Deep neural networks are trained with the boundary temperatures as inputs and Nusselt number, internal temperature or velocities as outputs. The number of domains which is essentially the stochastic dimension is 4, 8, 16 or 32. Rigorous training and testing process shows that the neural network is able to approximate the outputs to a reasonable accuracy. For a high stochastic dimension such as 32, it is computationally expensive to fit the PCE. This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method for uncertainty quantification with the number of simulations much fewer than that required for fitting the PCE, thus, saving the computational cost

Issues before the thirteenth finance commission.

The Thirteenth Finance Commission faces challenging times. Despite improvement, the fiscal situation continues to be a matter of concern when off budget liabilities and other fiscal risks are considered. In the changing situation of increasing oil prices on the one hand and surge in capital flows on the other, calibrating the transfer system in tune with counter-cyclical fiscal policy stance is a formidable challenge. The paper argues that irrespective of the wording of the Terms of Reference (ToR), the Commission would do well to focus on its primary task of recommending transfers to serve the objective of equity and incentives. While it is required to take into account a number of considerations, the focus should be on the transfer system. As an impartial body, the Commission should make a fair assessment of the union as well as state governments, ignoring the asymmetries in the wording of the ToR. As regards the transfer system itself is concerned, the paper argues that although it may be difficult to make drastic changes in the relative shares of the states, the Commission should give up the gap filling approach. Instead, after recommending the tax devolution, the Commission should recommend grants to fully equalise expenditures on elementary education and basic healthcare. It is also possible to incentivise the transfer system for even those states that have a better record of providing education and healthcare to improve quality of these services. If necessary, the tax devolution percentage can be appropriately adjusted to ensure equalisation of social services. The paper is a revised and edited version of one that was presented in the seminar on Issues before the Thirteenth Finance Commission held at the National Institute of Public Finance and Policy (NIPFP) on May 23-24, 2008.