LMI-based algorithm for strictly positive real systems with static output feedback

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

Optimal Frequency Range Selection for PZT Transducers in Impedance-Based SHM Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Robust state-derivative pole placement LMI-based designs for linear systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Robust State-Derivative Feedback LMI-Based Designs for Linear Descriptor Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fatigue and Fluoride Corrosion on Streptococcus mutans Adherence to Titanium-Based Implant/Component Surfaces

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

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

New analytical procedure based on a cellulose bag and ionic exchanger with p-aminobenzoic acid groups for differentiation of labile and inert metal species in aquatic systems

A new procedure was developed for the in situ characterization of the lability of metal species in aquatic systems by using a system equipped with a diffusion membrane and cellulose organomodified with p-aminobenzoic acid groups (DM-Cell-PAB). To this end, the DM-Cell-PAB system was prepared by adding cellulose organomodified with p-aminobenzoic acid groups (Cell-PAB) to pre-purified cellulose bags. After the DM-Cell-PAB system was sealed, it was examined in the laboratory to evaluate the influence of complexation time, mass of exchanger, pH, metal ions (Cu, Cd, Fe, Mn, and Ni), and concentration of organic matter on the relative lability of metal species. It was found that the pH and kinetics strongly influence the process of metal complexation by the DM-Cell-PAB system. At all pH levels, Cd, Mn, and Ni showed lower complexation with Cell-PAB resin than Cu and Fe metals. Note that relative lability of metals complexed to aquatic humic substances (AHS) in the presence of Cell-PAB resin showed the following order: Cu congruent to Fe >> Ni > Mn=Cd. The results presented here also indicate that increasing the AHS concentration decreases the lability of metal species by shifting the equilibrium to AHS-metal complexes. Our results indicate that the system under study offers an interesting alternative that can be applied to in situ experiments for differentiation of labile and inert metal species in aquatic systems.

A new approach for determining stability constants of metal ion complexes in aquatic systems based on flow injection-ion exchange-flame atomic absorption spectrometry coupling

A method based an ion exchange(IE)-atomic absorption spectrometry(AAS) coupled by flow techniques, allowing the determination of formation constants of, at least, the first species of complex systems, in aqueous solution, was developed.The IE-AAS coupling reduces significantly the number of experimental steps in comparison with IE batch methods, resulting in an important increase in analytical rate. The method is simple both from experimental and computational points of view, making possible its utilization by workers without special expertise in the field of complex equilibria in solution. on the other hand, taking into account mainly the amount of hollow cathode lamps available to date, the developed procedure may be applied, within certain limitations, to the study of many systems whose features prevent the use of traditional approaches.

Methodology for load compensation in four-wire electrical power systems based on instantaneous sampling

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and other in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the determination of the compensation susceptances is based on the instantaneous values of load currents. The results are obtained using the MatLab-Simulink enviroment

Robust state-derivative feedback LMI-based designs for multivariable linear systems

In some practical problems, for instance in the control systems for the suppression of vibration in mechanical systems, the state-derivative signals are easier to obtain than the state signals. New necessary and sufficient linear matrix inequalities (LMI) conditions for the design of state-derivative feedback for multi-input (MI) linear systems are proposed. For multi-input/multi-output (MIMO) linear time-invariant or time-varying plants, with or without uncertainties in their parameters, the proposed methods can include in the LMI-based control designs the specifications of the decay rate, bounds on the output peak, and bounds on the state-derivative feedback matrix K. These design procedures allow new specifications and also, they consider a broader class of plants than the related results available in the literature. The LMIs, when feasible, can be efficiently solved using convex programming techniques. Practical applications illustrate the efficiency of the proposed methods.

A comparative study of thermal conductivity in ZnO- and SnO2-based varistor systems

We performed a comparative study of electrical and thermal properties of ZnO- and SnO2-based varistor. The electrical properties of commercial ZnO-based varistor are equivalent to that found in SnO2-based varistor system. In spite of this, the SnO2 showed a thermal conductivity higher than commercial samples of ZnO-based varistor, which allied with its simpler microstructure and lower dopant concentration is a remarkable result that point out to the use of this system to compete commercially with ZnO-based varistor devices.

MnO2 influence on the electrical properties of SnO2-based ceramic systems

Tin dioxide is an n-type semiconductor that when doped with other metallic oxides exhibits non-linear electric behavior with high non-linear coefficient values typical of a varistor. In this work, electrical properties of the SnO2.CoO.Ta2O5 and SnO2.CoO.MnO2.Ta2O5 ceramics systems were studied with the objective of analyzing the influence of MnO2 on sintering behavior and electrical properties of these systems. The compacts were prepared by powder mixture process and sintered at 1300°C for 1 hour, in air, using a constant heating rate of 10°C/min. The morphological and structural properties were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The densities of the sintered ceramics were measured using the Archimedes method. The SnO2.CoO.Ta2O5 and SnO2.CoO.MnO2.Ta2O5 systems presented breakdown fields (Eb) about 3100 V.cm-1 and 3800 V.cm-1, respectively, and non-linear coefficient (α) about 10 and 20, respectively.

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

A petri net based timing model for hardware/software co-design of digital systems

We have recently proposed an extension to Petri nets in order to be able to directly deal with all aspects of embedded digital systems. This extension is meant to be used as an internal model of our co-design environment. After analyzing relevant related work, and presenting a short introduction to our extension as a background material, we describe the details of the timing model we use in our approach, which is mainly based in Merlin's time model. We conclude the paper by discussing an example of its usage. © 2004 IEEE.

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.