Showing papers by "CHESF published in 2004"
TL;DR: The Brazilian Solar Resource Atlas CD-ROM as mentioned in this paper is a tool for solar system design, delivering information that relies on a large interactive database, including, also, a modern device for calculating the solar radiation collected by planes inclined at different tilt angles or tracking the sun around one or two axes.
15 citations
1 Jan 2004
TL;DR: In this article, the authors presented a conceptual model of human errors scenarios for the electrical power industry using a knowledge acquisition method (KOD) to build a process control simulator with which it was possible to study the user behaviour when dealing with safety critical situations.
Abstract: This paper presents the process employed in obtaining a conceptual model of human errors scenarios for the electrical power industry. The model presented results from the analysis of the industry’s reports on human and operational errors using a knowledge acquisition method (KOD). These scenarios will be used to build a process control simulator with which it will be possible to study the user behaviour when dealing with safety critical situations. From these studies it will be extracted a cognitive model of the user behaviour when working under critical situations to be incorporated into a method for the conception of user interfaces based upon ergonomic concerns (MCIE).
2 citations
22 Nov 2004
TL;DR: In this article, a non-linear Auto-Regressive Exogenous-Input model (NARXM) for river flow forecasting by an output-updating procedure is presented based on the structure of a constructive neural network (NSRBN).
Abstract: A non-linear Auto-Regressive Exogenous-input model (NARXM) for river flow forecasting by an output-updating procedure is presented This updating procedure is based on the structure of a constructive neural network (NSRBN -A Non-linear Sigmoidal Regression Blocks Networks) The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the routing (SSARR-Streamflow Synthesis And Reservoir Regulation) conceptual model operating on the Sao Francisco River having different discharge conditions The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure