The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A time series sequestration and storage model of atmospheric carbon dioxide

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multistage Transmission Expansion Planning Considering Fixed Series Compensation Allocation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Lumped element modeling of operational structures by inverting the mobility models

This paper is concerned with what a source precisely sees when it drives a receiver such as a continuous structural object. An equivalent lumped element system consisting of masses, springs and dampers is developed to visually represent the operational structural dynamics of a single-input structure at the driving point. The development is solely based on the mobility model of the driving point response. The mobility model is mathematically inverted to give the impedance model that is suitable for lumped element modeling. The two types of structures studied are unconstrained inertial objects and constrained resilient objects. The lumped element systems presented suggest a new view to dynamics that a single-input flexible structure in operation can be decomposed into the two subsystems: a base system of single degree of freedom (or of a mass for an inertial object) whose mass is in contact with the source and an appendage system consisting of a series of oscillators each of which is attached to the base mass. The driving point response is a result of the coupling between the two subsystems. (C) 2014 Elsevier Ltd. All rights reserved.

Animal models in chronic obstructive pulmonary disease-an overview

Chronic obstructive pulmonary disease (COPD) is characterized by progressive airway obstruction resultant from an augmented inflammatory response of the respiratory tract to noxious particles and gases. Previous reports present a number of different hypotheses about the etiology and pathophysiology of COPD. The generating mechanisms of the disease are subject of much speculation, and a series of questions and controversies among experts still remain. In this context, several experimental models have been proposed in order to broaden the knowledge on the pathophysiological characteristics of the disease, as well as the search for new therapeutic approaches for acute or chronically injured lung tissue. This review aims to present the main experimental models of COPD, more specifically emphysema, as well as to describe the main characteristics, advantages, disadvantages, possibilities of application, and potential contribution of each of these models for the knowledge on the pathophysiological aspects and to test new treatment options for obstructive lung diseases.

Despacho ótimo de potências ativa e reativa de sistema elétricos multi-áreas considerando restrições físicas, econômicas e ambientais =: Envronmentally constrained active-reactive optimal power flow-a compromising strategy for economic-emission dispatch and a multi-area paradigm

Pós-graduação em Engenharia Elétrica - FEIS

The effect of power-ultrasound on the pretreatment of acidified aqueous solutions of banana flower-stalk: Structural, chemical and statistical analysis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multiseries Lie groups and asymptotic modules for characterizing and solving integrable models

A multiseries integrable model (MSIM) is defined as a family of compatible flows on an infinite-dimensional Lie group of N-tuples of formal series around N given poles on the Riemann sphere. Broad classes of solutions to a MSIM are characterized through modules over rings of rational functions, called asymptotic modules. Possible ways for constructing asymptotic modules are Riemann-Hilbert and ∂̄ problems. When MSIM's are written in terms of the group coordinates, some of them can be contracted into standard integrable models involving a small number of scalar functions only. Simple contractible MSIM's corresponding to one pole, yield the Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy. Two-pole contractible MSIM's are exhibited, which lead to a hierarchy of solvable systems of nonlinear differential equations consisting of (2 + 1) -dimensional evolution equations and of quite strong differential constraints. © 1989 American Institute of Physics.

Influence of double excitation AC/DC in the reactive power demand of 3-phase transformers

In this work it is discussed the performance of the reactive power demand in three-leg transformer core and three-phase transformer bank, under different conditions of AC/DC double excitation. In order to analyse the influence of double excitation in reactive power theoretically a mathematical model was developed considering the mutual coupling between phases and the magnetic nonlinearity. The validity of the proposed model is verified by means of the experimental and simulated results.

Representation of transmission lines: comparison of models and parameters distributed discrete parameters

A transmission line is characterized by the fact that its parameters are distributed along its length. This fact makes the voltages and currents along the line to behave like waves and these are described by differential equations. In general, the differential equations mentioned are difficult to solve in the time domain, due to the convolution integral, but in the frequency domain these equations become simpler and their solutions are known. The transmission line can be represented by a cascade of π circuits. This model has the advantage of being developed directly in the time domain, but there is a need to apply numerical integration methods. In this work a comparison of the model that considers the fact that the parameters are distributed (Universal Line Model) and the fact that the parameters considered concentrated along the line (π circuit model) using the trapezoidal integration method, and Simpson's rule Runge-Kutta in a single-phase transmission line length of 100 km subjected to an operation power. © 2003-2012 IEEE.

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)

Application of Volterra series in nonlinear mechanical system identification and in structural health monitoring problems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimação espaço-temporal das perdas não técnicas no sistema de distribuição de energia elétrica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)