A marker-and-cell approach to free surface 2-D multiphase flows

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

Nonperturbative approach to potentials in impenetrable boxes based on quasi-exact solvability

In this work we develop an approach to obtain analytical expressions for potentials in an impenetrable box. In this kind of system the expression has the advantage of being valid for arbitrary values of the box length, and respect the correct quantum limits. The similarity of this kind of problem with the quasi exactly solvable potentials is explored in order to accomplish our goals. Problems related to the break of symmetries and simultaneous eigenfunctions of commuting operators are discussed.

Dielectric spectroscopy on Bi3Zn2Sb3O14 ceramic: an approach based on the complex impedance

The dielectric properties and loss of Bi1.5ZnSb1.5O7 a poor-semiconducting ceramic were investigated by impedance spectroscopy, in the frequency range from 5 Hz to 13 MHz. Electric measurements were performed from 100 to 700 degreesC. Pyrochlore type phase was synthesized by the polymeric precursor method. Dense ceramic with 97% of the theoretical density was prepared by sintering via constant heating rate. The dielectric permittivity dependence as a function of frequency and temperature showed a strong dispersion at frequency lower than 10 kHz. The losses (tan delta) exhibit slight dependence with the frequency at low temperatures presenting a strong increase at temperatures higher than 400 degreesC. A decrease of the loss magnitude occurs with increasing frequency. Relaxation times were extracted using the dielectric functions Z(omega) and M(omega). The plots of the relaxation times tau(Z'), and tau(M) as a function of temperature follow the Arrhenius law, where a single slope is observed with activation energy values equal to 1.38 and 1.37 eV, respectively. (C) 2003 Elsevier Ltd. All rights reserved.

Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

A new approach to the solution of the optimal power flow problem based on the modified Newton's method associated to an augmented Lagrangian function

This paper presents a new algorithm for optimal power flow problem. The algorithm is based on Newton's method which it works with an Augmented Lagrangian function associated with the original problem. The function aggregates all the equality and inequality constraints and is solved using the modified-Newton method. The test results have shown the effectiveness of the approach using the IEEE 30 and 638 bus systems.

Accountability in Smart Microgrids Based on Conservative Power Theory

Smart microgrids offer a new challenging domain for power theories and metering techniques because they include a variety of intermittent power sources which positively impact on power flow and distribution losses but may cause voltage asymmetry and frequency variation. In smart microgrids, the voltage distortion and asymmetry in presence of poly-phase nonlinear loads can be also greater than in usual distribution lines fed by the utility, thus affecting measurement accuracy and possibly causing tripping of protections. In such a context, a reconsideration of power theories is required since they form the basis for supply and load characterization. A revision of revenue metering techniques is also suggested to ensure a correct penalization of the loads for their responsibility in generating reactive power, voltage asymmetry, and distortion. This paper shows that the conservative power theory provides a suitable background to cope with smart grids characterization and metering needs. Simulation and experimental results show the properties of the proposed approach.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.

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.

Modeling approach based on experimental results for prediction of measurement errors in energy meters

This paper presents a general modeling approach to investigate and to predict measurement errors in active energy meters both induction and electronic types. The measurement error modeling is based on Generalized Additive Model (GAM), Ridge Regression method and experimental results of meter provided by a measurement system. The measurement system provides a database of 26 pairs of test waveforms captured in a real electrical distribution system, with different load characteristics (industrial, commercial, agricultural, and residential), covering different harmonic distortions, and balanced and unbalanced voltage conditions. In order to illustrate the proposed approach, the measurement error models are discussed and several results, which are derived from experimental tests, are presented in the form of three-dimensional graphs, and generalized as error equations. © 2009 IEEE.

A practical approach for biometric authentication based on smartcards

Cryptographic systems are safe. However, the management of cryptographic keys of these systems is a tough task. They are usually protected by the use of password-based authentication mechanisms, which is a weak link on conventional cryptographic systems, as the passwords can be easily copied or stolen. The usage of a biometric approach for releasing the keys is an alternative to the password-based mechanisms. But just like passwords, we need mechanisms to keep the biometrical signal safe. One approach for such mechanism is to use biometrical key cryptography. The cryptographic systems based on the use of biometric characteristics as keys are called biometrical cryptographic systems. This article presents the implementation of Fuzzy Vault, a biometrical cryptographic system written in Java, along with its performance evaluation. Fuzzy Vault was tested on a real application using smartcards.

Packaging controlled experiments using an evolutionary approach based on ontology

A body of knowledge in Software Engineering requires experiments replications. The knowledge generated by a study is registered in the so-called lab package, which, must be reviewed by an eventual research group with the intention to replicate it. However, researchers face difficulties reviewing the lab package, what leads to problems in share knowledge among research groups. Besides that, the lack of standardization is an obstacle to the integration of the knowledge from an isolated study in a common body of knowledge. In this sense, ontologies can be applied, since they can be seen as a standard that promotes the shared understanding of the experiment information structure. In this paper, we present a workflow to generate lab packages based on EXPEiiQntology, an ontology of controlled experiments domain. In addition, by means of lab packages instantiation, it is possible to evolve the ontology, in order to deal with new concepts that may appear in different lab packages. The iterative ontology evolution aims at achieve a standard that is able to accommodate different lab packages and, hence, facilitate to review and understand their content.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.