Mas-Colell, Whinston and Green Versus Scitovsky on Profit and Utility Maximization

I contrast the theoretical foundation of profit maximization of Mas-Colell, Whinston and Green’s “Microeconomics” against that provided by Scitovsky in a paper of 1943. Whereas Mas-Colell, Whinston and Green try to show that profit maximization can be derived from utility maximization, Scitovsky categorically states the contrary view. I argue, first, that the foundation provided by Mas-Colell, Whinston and Green is not sound and, secondly, that Scitovsky’s line of reasoning opens a better way to model business behavior.

Sliding Mode Control Strategy for Wind Turbine Power Maximization

The efficiency of the wind power conversions systems can be greatly improved using an appropriate control algorithm. In this work, a sliding mode control for variable speed wind turbine that incorporates a doubly fed induction generator is described. The electrical system incorporates a wound rotor induction machine with back-to-back three phase power converter bridges between its rotor and the grid. In the presented design the so-called vector control theory is applied, in order to simplify the electrical equations. The proposed control scheme uses stator flux-oriented vector control for the rotor side converter bridge control and grid voltage vector control for the grid side converter bridge control. The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. Finally simulated results show, on the one hand, that the proposed controller provides high-performance dynamic characteristics, and on the other hand, that this scheme is robust with respect to the uncertainties that usually appear in the real systems.

An adaptive sliding mode control law for the power maximization of the wind turbine system

POWERENG 2011

Diferenças de expectativas em auditorias independentes: estudo comparativo sobre expectation gap, em cenário globalizado

O presente estudo objetiva analisar as características das diferenças de expectativas entre o público geral e os auditores independentes, no que diz respeito às demonstrações contábeis. Para isso, incorreu-se em uma pesquisa de artigos científicos em que os autores investigam o problema, cada um em determinado país, e as causas de sua ocorrência. Essa análise da literatura permitiu verificar as similaridades e sugestões para reduzir o fenômeno, em cenário globalizado, e compará-las. Os principais achados demonstram que, de maneira geral, os problemas são globalmente relacionados, assim como as sugestões, e que se torna essencial medidas para amenizar o problema. Tanto os auditores independentes quanto os usuários das demonstrações contábeis tem conhecimento da existência dessa diferença de expectativa, sendo uma ameaça para o bom andamento de uma economia capitalista o desconforto dos usuários caso ocorra à manutenção dessa diferença de expectativa. Dessa maneira, uma mudança na estrutura do cenário atual das empresas de auditoria independente torna-se fundamental.

A Novel Scheduling Index Rule Proposal for QoE Maximization in Wireless Networks

This paper deals with the resource allocation problem aimed at maximizing users' perception of quality in wireless channels with time-varying capacity. First of all, we model the subjective quality-aware scheduling problem in the framework of Markovian decision processes. Then, given that the obtaining of the optimal solution of this model is unachievable, we propose a simple scheduling index rule with closed-form expression by using a methodology based on Whittle approach. Finally, we analyze the performance of the achieved scheduling proposal in several relevant scenarios, concluding that it outperforms the most popular existing resource allocation strategies.

An online expectation-maximisation algorithm for nonnegative matrix factorisation models

In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximation of our online EM algorithm. We show the performance of the proposed method with two numerical examples. © 2012 IFAC.

A Monte Carlo expectation maximisation algorithm for multiple target tracking

In this paper, we present an expectation-maximisation (EM) algorithm for maximum likelihood estimation in multiple target models (MTT) with Gaussian linear state-space dynamics. We show that estimation of sufficient statistics for EM in a single Gaussian linear state-space model can be extended to the MTT case along with a Monte Carlo approximation for inference of unknown associations of targets. The stochastic approximation EM algorithm that we present here can be used along with any Monte Carlo method which has been developed for tracking in MTT models, such as Markov chain Monte Carlo and sequential Monte Carlo methods. We demonstrate the performance of the algorithm with a simulation. © 2012 ISIF (Intl Society of Information Fusi).

Two problems with variational expectation maximisation for time-series models

Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.