Experimental validation of a proposed single-phase five-level active rectifier operating with model predictive current control

This paper presents a model predictive current control applied to a proposed single-phase five-level active rectifier (FLAR). This current control strategy uses the discrete-time nature of the active rectifier to define its state in each sampling interval. Although the switching frequency is not constant, this current control strategy allows to follow the reference with low total harmonic distortion (THDF). The implementation of the active rectifier that was used to obtain the experimental results is described in detail along the paper, presenting the circuit topology, the principle of operation, the power theory, and the current control strategy. The experimental results confirm the robustness and good performance (with low current THDF and controlled output voltage) of the proposed single-phase FLAR operating with model predictive current control.

Using relational algebra on the specification of real world ETL processes

Modeling Extract-Transform-Load (ETL) processes of a Data Warehousing System has always been a challenge. The heterogeneity of the sources, the quality of the data obtained and the conciliation process are some of the issues that must be addressed in the design phase of this critical component. Commercial ETL tools often provide proprietary diagrammatic components and modeling languages that are not standard, thus not providing the ideal separation between a modeling platform and an execution platform. This separation in conjunction with the use of standard notations and languages is critical in a system that tends to evolve through time and which cannot be undermined by a normally expensive tool that becomes an unsatisfactory component. In this paper we demonstrate the application of Relational Algebra as a modeling language of an ETL system as an effort to standardize operations and provide a basis for uncommon ETL execution platforms.

Improvign mixing processes with the help of numerical tools

In several industrial applications, highly complex behaviour materials are used together with intricate mixing processes, which difficult the achievement of the desired properties for the produced materials. This is the case of the well-known dispersion of nano-sized fillers in a melt polymer matrix, used to improve the nanocomposite mechanical and/or electrical properties. This mixing is usually performed in twin-screw extruders, that promote complex flow patterns, and, since an in loco analysis of the material evolution and mixing is difficult to perform, numerical tools can be very useful to predict the evolution and behaviour of the material. This work presents a numerical based study to improve the understanding of mixing processes. Initial numerical studies were performed with generalized Newtonian fluids, but, due to the null relaxation time that characterize this type of fluids, the assumption of viscoelastic behavior was required. Therefore, the polymer melt was rheologically characterized, and, a six mode Phan-Thien-Tanner and Giesekus models were used to fit the rheological data. These viscoelastic rheological models were used to model the process. The conclusions obtained in this work provide additional and useful data to correlate the type and intensity of the deformation history promoted to the polymer nanocomposite and the quality of the mixing obtained.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.