PARTITIONED ANALYSIS FOR DIMENSIONALLY-HETEROGENEOUS HYDRAULIC NETWORKS

In this work an iterative strategy is developed to tackle the problem of coupling dimensionally-heterogeneous models in the context of fluid mechanics. The procedure proposed here makes use of a reinterpretation of the original problem as a nonlinear interface problem for which classical nonlinear solvers can be applied. Strong coupling of the partitions is achieved while dealing with different codes for each partition, each code in black-box mode. The main application for which this procedure is envisaged arises when modeling hydraulic networks in which complex and simple subsystems are treated using detailed and simplified models, correspondingly. The potentialities and the performance of the strategy are assessed through several examples involving transient flows and complex network configurations.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Normal Versus Pathological Voice Signals Using Wavelet Analysis and Least Squares Support-Vector Machines

State of Sao Paulo Research Foundation (FAPESP)

Remote control of CNC machines using the CyberOPC communication system over public networks

During the last few years, the evolution of fieldbus and computers networks allowed the integration of different communication systems involving both production single cells and production cells, as well as other systems for business intelligence, supervision and control. Several well-adopted communication technologies exist today for public and non-public networks. Since most of the industrial applications are time-critical, the requirements of communication systems for remote control differ from common applications for computer networks accessing the Internet, such as Web, e-mail and file transfer. The solution proposed and outlined in this work is called CyberOPC. It includes the study and the implementation of a new open communication system for remote control of industrial CNC machines, making the transmission delay for time-critical control data shorter than other OPC-based solutions, and fulfilling cyber security requirements.

Biohydrogen production in anaerobic fluidized bed reactors: Effect of support material and hydraulic retention time

This study evaluated two different support materials (polystyrene and expanded clay) for biohydrogen production in an anaerobic fluidized bed reactor (AFBR) treating synthetic wastewater containing glucose (4000 mg L(-1)). The AFBRs contained either polystyrene (R1) or expanded clay (R2) as support materials were inoculated with thermally pre-treated anaerobic sludge and operated at a temperature of 30 degrees C and a pH of approximately 5.5. The AFBRs were operated with a range of hydraulic retention times (HRTs) between 1 and 8 h. For R1 with an HRT of 2 h, the maximum hydrogen yield (HY) was 1.90 mol H(2) mol(-1) glucose, with 0.805 mg of biomass (as total volatile solids, or TVS) attached to each g of polystyrene. For R2 operated at an HRT of 2 h, the maximum HY was 2.59 mol H(2) moll glucose, with 1.100 mg of attached biomass (as TVS) g(-1) expanded clay. The highest hydrogen production rates (HPR) were 0.95 and 1.21 L h(-1) L(-1) for R1 and R2, respectively, using an HRT of 1 h. The H(2) content increased from 16-47% for R1 and from 22-51% for R2. No methane was detected in the biogas produced throughout the period of AFBR operation. These results show that the values of HY, HPR, H(2) content, and g of attached biomass g(-1) support material were all higher for AFBRs containing expanded clay than for reactors containing polystyrene. (C) 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

Comparison Between Voltage Control Structures of Synchronous Machines

This paper compares the behaviour of two different control structures of automatic voltage regulators of synchronous machines equipped with static excitation systems. These systems have a fully controlled thyristor bridge that supplies DC current to the rotor winding. The rectifier bridge is fed by the stator terminals through a step-down transformer. The first control structure, named ""Direct Control"", has a single proportional-integral (PI) regulator that compares stator voltage setpoint with measured voltage and acts directly on the thyristor bridge`s firing angle. This control structure is usually employed in commercial excitation systems for hydrogenerators. The second structure, named ""Cascade Control"", was inspired on control loops of commercial DC motor drives. Such drives employ two PIs in a cascade arrangement, the external PI deals with the motor speed while the internal one regulates the armature current. In the adaptation proposed, the external PI compares setpoint with the actual stator voltage and produces the setpoint to the internal PI-loop which controls the field current.

Unsaturated hydraulic conductivity of an Oxisol, using a neutron probe.

Unsaturated hydraulic conductivity of an Oxisol, using a neutron probe. The objective of this study was to determine the unsaturated hydraulic conductivity, using a neutron probe, of a clay sandy Oxisol. The Study was carried out in the city of Piracicaba, kite of Sao Paulo, Brazil (22 degrees 42` 43.3 `` S, 47 degrees`37` 10.4 `` W, 546 m). The dimensions of the experimental plot were 45 In x 15 m, in which 40 aluminum tubes were installed in order to access a neutron probe to measure the soil water content at the depths of 0.2, 0.4, 0.6, 0.8 and 1.0 m and, then, calculate the soil water storage of the 0 - 1.0 m soil layer. The distribution of these tubes was made in grids of four columns by ten rows in spacing of 5 x 5 m. The K(theta) functions were determined in the 40 points from regression analyses of theta as function Int and h(z) as a function of Int, being K the hydraulic conductivity, theta the volumetric soil water content, h(z) the soil water storage in the 0 - Z m layer, and t the soil water redistribution time. The neutron probe proved to be an efficient equipment in determining soil water contents, in the instantaneous profile method for determination of the K(theta) function in homogeneous soil.

Alternative Analytical Expressions for the General van Genuchten-Mualem and van Genuchten-Burdine Hydraulic Conductivity Models

The van Genuchten expressions for the unsaturated soil hydraulic properties, first published in 1980, are used frequently in various vadose zone flow and transport applications assuming a specific relationship between the m and n soil hydraulic parameters. By comparison, probably because of the complexity of the hydraulic conductivity equations, the more general solutions with independent m and n values are rarely used. We expressed the general van Genuchten-Mualem and van Genuchten-Burdine hydraulic conductivity equations in terms of hypergeometric functions, which can be approximated by infinite series that converge rapidly for relatively large values of the van Genuchten-Mualem parameter n but only very slowly when n is close to one. Alternative equations were derived that provide very close approximations of the analytical results. The newly proposed equations allow the use of independent values of the parameters m and n in the soil water retention model of van Genuchten for subsequent prediction of the van Genuchten-Mualem and van Genuchten-Burdine hydraulic conductivity models, thus providing more flexibility in fitting experimental pressure-head-dependent water content, theta(h), and hydraulic conductivity, K(h), or K(theta) data.

Measuring Hydraulic Conductivity to Wilting Point Using Polymer Tensiometers in an Evaporation Experiment

The polymer tensiometer is a novel instrument to measure soil water pressure heads from saturation to permanent wilting conditions. We used tensiometers of this type in an experiment to determine the hydraulic properties of evaporating soil samples in the laboratory. Relative errors in the hydraulic conductivity function in the wet part were high due to the relatively low accuracy of the pressure transducers, resulting in a large uncertainty in the hydraulic gradient and therefore in the calculated hydraulic conductivity. In the dry part, the error related to this accuracy was on the same order of magnitude as the error related to balance accuracy. Therefore, the method can be assumed adequate for measuring soil hydraulic properties except under very wet conditions. In our experiments, relative error and bias increased significantly at pressure heads less negative than -1 m.

Effectiveness of the standard evaluation method for hydraulic nozzles employed in stored grain protection trials

In stored grains, smaller depositions and great variation with respect to theoretical insecticide doses are frequently found. The objective of this work was to study the effectiveness of the standard method (ISO 5682/1-1996) employed to evaluate hydraulic nozzles used in stored corn and wheat grain protection experiments. The transversal volumetric distribution and droplet spectrum of a model TJ-60 8002EVS nozzle were determined in order to calibrate a spraying system for an application rate of 5 L/t and to obtain theoretical concentrations of 10 and 0.5 mg/kg of fenitrothion and esfenvalerate, respectively. After treatment, the corn and wheat grains were processed and deposition was analyzed by gas chromatography. The type of grain did not have any influence on insecticide deposition and was dependent upon insecticide only. The insecticide deposits on the grains only reached 42.1 and 38.2% of the intended theoretical values for fenitrothion and esfenvalerate concentrations, respectively. These results demonstrate the ineffectiveness of the standard evaluation method for hydraulic nozzles employed in stored grain protection experiments.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Checking Completeness of Tests for Finite State Machines

In testing from a Finite State Machine (FSM), the generation of test suites which guarantee full fault detection, known as complete test suites, has been a long-standing research topic. In this paper, we present conditions that are sufficient for a test suite to be complete. We demonstrate that the existing conditions are special cases of the proposed ones. An algorithm that checks whether a given test suite is complete is given. The experimental results show that the algorithm can be used for relatively large FSMs and test suites.

Effect of propolis gel on the in vitro reduction of dentin permeability

OBJECTIVE: The aim of this study was to evaluate the capacity of potassium oxalate, fluoride gel and two kinds of propolis gel to reduce the hydraulic conductance of dentin, in vitro. MATERIAL AND METHODS: The methodology used for the measurement of hydraulic conductance of dentin in the present study was based on a model proposed in literature. Thirty-six 1-mm-thick dentin discs, obtained from extracted human third molars were divided into 4 groups (n=9). The groups corresponded to the following experimental materials: GI-10% propolis gel, pH 4.1; GII-30% propolis gel; GIII-3% potassium oxalate gel, pH 4,1; and GIV-1.23% fluoride gel, pH 4.1, applied to the dentin under the following surface conditions: after 37% phosphoric acid and before 6% citric acid application. The occluding capacity of the dentin tubules was evaluated using scanning electron microscopy (SEM) at ×500, ×1,000 and ×2,000 magnifications. Data were analyzed statistically by two-way ANOVA and Tukey's test at 5% significance level. RESULTS: Groups I, II, III, IV did not differ significantly from the others in any conditions by reducing in hydraulic conductance. The active agents reduced dentin permeability; however they produced the smallest reduction in hydraulic conductance when compared to the presence of smear layer (P<0.05). The effectiveness in reducing dentin permeability did not differ significantly from 10% or 30% propolis gels. SEM micrographs revealed that dentin tubules were partially occluded after treatment with propolis. CONCLUSIONS: Under the conditions of this study, the application of 10% and 30% propolis gels did not seem to reduce the hydraulic conductance of dentin in vitro, but it showed capacity of partially obliterating the dentin tubules. Propolis is used in the treatment of different oral problems without causing significant great collateral effects, and can be a good option in the treatment of patients with dentin sensitivity.