Dynamic programming approach to structural optimization problem – numerical algorithm

In this paper a new shape optimization algorithm is presented. As a model application we consider state problems related to fluid mechanics, namely the Navier-Stokes equations for viscous incompressible fluids. The general approach to the problem is described. Next, transformations to classical optimal control problems are presented. Then, the dynamic programming approach is used and sufficient conditions for the shape optimization problem are given. A new numerical method to find the approximate value function is developed

Design of Prototype Dynamic Ac Power Machine with Equivalent Circuit Modeling (Torque Speed Curve of Induction Motor 1,1, Kw)

Squirrel cage induction motors are widely used in electric motor drives due to their satisfactory mechanical characteristics (torque, current, overloading) and small dimensions, as well as their low price. When starting an induction motor, a large current is required for magnetizing its core, which results in a low power factor, rotor power losses and a temperature rise in the windings. None of these parameters should reach values beyond certain limits until the motor reaches nominal speed. The speed of an induction motor 1,1kW is affected very little by fluctuations of voltage. The greater the supply voltage of the motor, the induction motor's speed will increase. The torque values (Tstart, TSmax and Tmax) are affected by the value of the motor supply voltage: (Vp-nl : 132.8, Tstart1 : 7.4, T S-max1 : 0.4, Tmax1 : 9.9) V, (Vp-nl : 127.0, Tstart2 : 4.8, T S-max1 : 0.3, Tmax1 : 8.4) V and (Vp-nl : 121.3, Tstart3 : 3.3, T S-max3 : 0.2, Tmax3 : 7.1) V. Stator current (IL-nl ; 2.5, 2.2, 1.9 ) Amp rises gradually on account of the increase in magnetising current (Im : 2.5, 2.2, 1.9) Amp. The magnetising current required to produce the stator flux. The component of the stator current which provides the ampere-turns balancing the rotor ampere-turns will steadily diminish as the rotor current (IL-nl) decrease with the increase in rotor speed (nr).&nbsp

Dynamic Shrinkage Processes

We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon a global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence among the local scale parameters. The resulting processes inherit the desirable shrinkage behavior of popular global-local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modeling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a P\'olya-Gamma scale mixture representation of the proposed process. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than competing methods, and apply the model for irregular curve-fitting of minute-by-minute Twitter CPU usage data. In addition, we develop an adaptive time-varying parameter regression model to assess the efficacy of the Fama-French five-factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that with the exception of the market risk, no other risk factors are significant except for brief periods

Dynamic structure function of a cold Fermi gas at unitarity

We present a theoretical study of the dynamic structure function of a resonantly interacting two-component Fermi gas at zero temperature. Our approach is based on dynamic many-body theory able to describe excitations in strongly correlated Fermi systems. The fixed-node diffusion Monte Carlo method is used to produce the ground-state correlation functions which are used as an input for the excitation theory. Our approach reproduces recent Bragg scattering data in both the density and the spin channel. In the BCS regime, the response is close to that of the ideal Fermi gas. On the BEC side, the Bose peak associated with the formation of dimers dominates the density channel of the dynamic response. When the fraction of dimers is large our theory departs from the experimental data, mainly in the spin channel.Peer ReviewedPostprint (published version

DEVELOPING, MODELLING AND MAPPING OF CRITICAL LOADS AND THEIR INPUT DATA STATUS REPORT ON THE CALL FOR EUROPEAN CRITICAL LOADS ON ACIDIFICATION AND EUTROPHICATION

Programme (ICP) on the Modelling and Mapping of Critical Levels and Loads and Air Pollution Effects, Risks and Trends, with the assistance of the secretaria

Dynamic Planar Embeddings of Dynamic Graphs

We present an algorithm to support the dynamic embedding in the plane of a dynamic graph. An edge can be inserted across a face between two vertices on the face boundary (we call such a vertex pair linkable), and edges can be deleted. The planar embedding can also be changed locally by flipping components that are connected to the rest of the graph by at most two vertices. Given vertices $u,v$, linkable$(u,v)$ decides whether $u$ and $v$ are linkable in the current embedding, and if so, returns a list of suggestions for the placement of $(u,v)$ in the embedding. For non-linkable vertices $u,v$, we define a new query, one-flip-linkable$(u,v)$ providing a suggestion for a flip that will make them linkable if one exists. We support all updates and queries in O(log$^2 n$) time. Our time bounds match those of Italiano et al. for a static (flipless) embedding of a dynamic graph. Our new algorithm is simpler, exploiting that the complement of a spanning tree of a connected plane graph is a spanning tree of the dual graph. The primal and dual trees are interpreted as having the same Euler tour, and a main idea of the new algorithm is an elegant interaction between top trees over the two trees via their common Euler tour.Comment: Announced at STACS'1