Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques - combines computer engineering concepts with areas like dermatology and oncology. Its objective is to discern images of skin cancer, specifically melanoma, from others that show only common spots or other types of skin diseases, using image recognition. This work makes use of the ABCDE visual rule, which is often used by dermatologists for melanoma identification, to define which characteristics are analyzed by the software. It then applies various algorithms and techniques, including an ellipse-fitting algorithm, to extract and measure these characteristics and decide whether the spot is a melanoma or not. The achieved results are presented with special focus on the adaptive decision-making and its effect on the diagnosis. Finally, other applications of the software and its algorithms are presented.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.

Thermodynamic Assessment of the Aluminum Corner of the Al-Fe-Mn-Si System

A new assessment of the aluminum corner of the quaternary Al-Fe-Mn-Si system has been made that extends beyond the COST-507 database. This assessment makes use of a recent, improved description of the ternary Al-Fe-Si system. In the present work, modeling of the Al-rich corner of the quaternary Al-Fe-Mn-Si system has been carried out by introducing Fe solubility into the so-called alpha-AlMnSi and beta-AlMnSi phases of the Al-Mn-Si system. A critical review of the data available on the quaternary system is presented and used for the extension of the description of these ternary phases into the quaternary Al-Fe-Mn-Si.

Optimization of preservative system in ophthalmic suspension with dexamethasone and polymyxin B

A simplex-lattice statistical project was employed to study an optimization method for a preservative system in an ophthalmic suspension of dexametasone and polymyxin B. The assay matrix generated 17 formulas which were differentiated by the preservatives and EDTA (disodium ethylene diamine-tetraacetate), being the independent variable: X-1 = chlorhexidine digluconate (0.010 % w/v); X-2 = phenylethanol (0.500 % w/v); X-3 = EDTA (0.100 % w/v). The dependent variable was the Dvalue obtained from the microbial challenge of the formulas and calculated when the microbial killing process was modeled by an exponential function. The analysis of the dependent variable, performed using the software Design Expert/W, originated cubic equations with terms derived from stepwise adjustment method for the challenging microorganisms: Pseudomonas aeruginosa, Burkholderia cepacia, Staphylococcus aureus, Candida albicans and Aspergillus niger. Besides the mathematical expressions, the response surfaces and the contour graphics were obtained for each assay. The contour graphs obtained were overlaid in order to permit the identification of a region containing the most adequate formulas (graphic strategy), having as representatives: X-1 = 0.10 ( 0.001 % w/v); X-2 = 0.80 (0.400 % w/v); X-3 = 0.10 (0.010 % w/v). Additionally, in order to minimize responses (Dvalue), a numerical strategy corresponding to the use of the desirability function was used, which resulted in the following independent variables combinations: X-1 = 0.25 (0.0025 % w/v); X-2 = 0.75 (0.375 % w/v); X-3 = 0. These formulas, derived from the two strategies (graphic and numerical), were submitted to microbial challenge, and the experimental Dvalue obtained was compared to the theoretical Dvalue calculated from the cubic equation. Both Dvalues were similar to all the assays except that related to Staphylococcus aureus. This microorganism, as well as Pseudomonas aeruginosa, presented intense susceptibility to the formulas independently from the preservative and EDTA concentrations. Both formulas derived from graphic and numerical strategies attained the recommended criteria adopted by the official method. It was concluded that the model proposed allowed the optimization of the formulas in their preservation aspect.

IDENTIFICATION OF THE STATE OF A WET SPOUTED BED THROUGH TIME-FREQUENCY ANALYSIS OF PRESSURE FLUCTUATION TIME SERIES

The feasibility of detecting instability in wet spouted beds via pressure fluctuation (PF) time-series analyses was investigated. Experiments were carried out in a cylindrical Plexiglas column of diameter 150 mm with a conical base of internal angle 60 degrees, an inlet orifice diameter of 25 mm and glass beads of diameter 2.4 mm. Transducers at several axial positions measured PF time series with incremental addition of aqueous sucrose solutions of different concentrations. Liquid addition affected the spouted bed dynamics, causing irregular spouting, increased voidage in the annulus, increased fountain height, irregular annulus height, channelling, agglomeration, and adhesion of particles to the column walls. Autocorrelations indicated the appearance of periodicities in the PF signals with increasing sucrose addition. Dominant peaks in power-spectral density developed at low frequencies with changing system dynamics. The results indicate that PF signals furnish relevant information on system dynamics, useful for monitoring and control of spouted bed operations such as particle coating and drying of paste-like materials.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

The universal newborn hearing screening in Brazil: From identification to intervention

Objective: The objective of the study is to investigate the results of the newborn hearing screening program carried out in a Public Hospital in Brazil, in the first 3 years regarding: (1) the prevalence of hearing impairment; (2) the influence of the universal hearing screening program on the age at which the diagnosis of hearing loss is defined; (3) the cost effectiveness of the program; (4) the outcomes, in terms of the age in which the hearing rehabilitation started. Methods: A descriptive study of the first 3 years after starting the universal newborn hearing screening in a Public Hospital of Bauru, Sao Paulo state, Brazil. The screening method consists of a two-stage screening approach with transient otoacoustic emissions (TOAE), conducted by an audiologist. If the outcome in the second-stage screening is REFER, the infant is submitted to diagnostic follow-up testing and intervention at the Audiology and Speech Pathology Clinic at the University of Sao Paulo, campus of Bauru. The evaluation of the costs of the universal newborn hearing screening program per each screened newborn (around 4000/year) was done based on a proposal by the National Center for Hearing Assessment and Management, of the Utah State University, United States of America. Results: 11,466 newborns were submitted to hearing screening, corresponding to 90.52% of the living newborns. The prevalence of sensorineural hearing loss was 0.96:1000. Of the 11 children with sensorineural hearing loss, eight children received hearing aids and five started the therapeutic process before the age of 1. Currently, four children between the ages of 11 months and 2 years old were submitted to cochlear implant surgery. The cost of hearing screening was US$7.00 and the annual cost of the universal newborn hearing screening program was US$26,940.47. Conclusion: The hospital-based universal newborn hearing screening carried out through the Brazilian National Health System is viable, with promising results. However, in a country such as Brazil, which presents large socio-economic differences, the same type of analyses should be performed in several regions, so as to take into account specific aspects, to implement the newborn hearing screening along with the Public System. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

A Lagrangian identification of major sources of moisture over Central Brazil and La Plata Basin

[1] This work examines the main sources of moisture over Central Brazil and La Plata Basin during the year through a new Lagrangian diagnosis method which identifies the humidity contributions to the moisture budget over a region. This methodology computes budgets of evaporation minus precipitation by calculating changes in the specific humidity along back-trajectories for the previous 10 d. The origin of all air masses residing over each region was tracked during a period of 5 years (2000-2004). These regions were selected because they coincide with two centers of action of a known dipole precipitation variability mode observed in different temporal scales (from intra seasonal up to inter decadal timescales) and are related to the climatic variability of the South American Monsoon System. The results suggested the importance of the tropical south Atlantic as a moisture source for Central Brazil, and of recycling for La Plata basin. It seems that the Tropical South Atlantic plays an important role as a moisture source for Central Brazil and La Plata basin along the year, particularly during the austral summer. The north Atlantic is also an additional source for both regions during the austral summer.