One important requirement of many real-time systems is the ability to undergo several mutually exclusive modes of operation. By means of a mode change the system changes its functionality over time, thus being able to adapt to changing environmental situations. In order to successfully include mode changes in real-time systems, a mode change protocol with well known real-time behaviour is necessary. In this paper we provide a new model and related schedulability analysis for mode changes in flexible real-time systems.

Real-time Fieldbuses are currently a signijcant issue in both process control and manufacturing areas. They constitute the base upon which real-time fault-tolerant distributed systems can be designed for these application areas. A potential large leap towards the use of Fieldbus in such timecritical applications lies in the evaluation of its temporal behuviour In particulal; an important problem associated with the Fieldbus FIP is its inability to guarantee the timing pe~ormance of sporadic trafJic. In this paper we develop the pre-run-time schedulability analysis of FIE bounding the worst case response time of the sporadic trafJic.

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This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.

In hydroelectric systems, water inflow is important to coordinate a cascade and define the energy price. This paper presents a method for managing inflow forecasting studies with a specific module for advanced assessment. The main goal is to provide a structure that facilitates the analysis of water inflow prediction models. A case study has been applied to five mathematical models based on linear regression, artificial neural networks, and hydrologic simulation. These models present daily and monthly inflow forecasts for a set of hydroelectric plants and monitoring stations. The benefits of the proposed method are analyzed in four situations: water inflow prediction, performance evaluation of a specific model, research tool for inflow forecasting, and comparison tool for distinct models. The results show that implementation of the proposed method provides a useful tool for managing inflow forecasting studies and analyzing models. Therefore, it can assist researchers and engineering professionals alike by improving the quality of water inflow predictions.