Surface characterization of feldspathic ceramic using ATR FT-IR and ellipsometry after various silanization protocols

Objectives. This study characterized the feldspathic ceramic surfaces after various silanization protocols.Methods. Ceramic bars (2 mm x 4 mm x 10 mm) (N = 18) of feldpathic ceramic (VM7, VITA Zahnfabrik) were manufactured and finished. Before silane application, the specimens were ultrasonically cleaned in distilled water for 10 min. The ceramic specimens were randomly divided into nine groups (N = 2 per group) and were treated with different silane protocols. MPS silane (ESPE-Sil, 3M ESPE) was applied to all specimens and left to react at 20 degrees C for 2 min (G20). After drying, the specimens were subjected to heat treatment in an oven at 38 degrees C (G38), 79 degrees C (G79) or 100 degrees C (G100) for 1 min. Half of the specimens of each group were rinsed with water at 80 degrees C for 15 s (G20B, G38B, G79B, G100B). The control group (GC) received no silane. Attenuated total reflection infrared Fourier transform analysis (ATR FT-IR) was performed using a spectrometer. Thickness of silane layer was measured using a spectroscopic ellip-someter working in the lambda = 632.8 nm (He-Ne laser) at 70 degrees incidence angle. Surface roughness was evaluated using an optical profilometer. Specimens were further analyzed under the Scanning Electron Microscopy (SEM) to observe the topographic patterns.Results. ATR FT-IR analysis showed changes in Si-O peaks with enlarged bands around 940 cm(-1). Ellipsometry measurements showed that all post-heat treatment actions reduced the silane film thickness (30.8-33.5 nm) compared to G20 (40 nm). The groups submitted to rinsing in hot water (B groups) showed thinner silane films (9.8-14.4 nm) than those of their corresponding groups (without washing) (30.8-40 nm). Profilometer analysis showed that heat treatments (Ra approximate to 0.10-0.19 mu m; Rq approximate to 0.15-0.26 mu m) provided a smoother surface than the control group (Ra approximate to 0.48 mu m; Rq approximate to 0.65 mu m). Similar patterns were also observed in SEM images.Significance. Heat treatment after MPS silane application improved the silane layer network. Rinsing with boiling water eliminated the outmost unreacted regions of the silane yielding to thinner film thicknesses. (C) 2011 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

A revised taxonomy for time warp based distributed synchronization protocols

This paper presents an improved and updated taxonomy for Time Warp based distributed synchronization protocols. This taxonomy aims to allow the grouping of several optmistic distributed simulation synchronization protocols, with the objective to facilitate the task to decide which protocol is better for a specific simulation.

Algorithms for network piecewise-linear programs: A comparative study

Piecewise-Linear Programming (PLP) is an important area of Mathematical Programming and concerns the minimisation of a convex separable piecewise-linear objective function, subject to linear constraints. In this paper a subarea of PLP called Network Piecewise-Linear Programming (NPLP) is explored. The paper presents four specialised algorithms for NPLP: (Strongly Feasible) Primal Simplex, Dual Method, Out-of-Kilter and (Strongly Polynomial) Cost-Scaling and their relative efficiency is studied. A statistically designed experiment is used to perform a computational comparison of the algorithms. The response variable observed in the experiment is the CPU time to solve randomly generated network piecewise-linear problems classified according to problem class (Transportation, Transshipment and Circulation), problem size, extent of capacitation, and number of breakpoints per arc. Results and conclusions on performance of the algorithms are reported.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Managing large scale virtual environments using portals

This article describes a technique for Large Scale Virtual Environments (LSVEs) partitioning in hexagon cells and using portal in the cell interfaces to reduce the number of messages on the network and the complexity of the virtual world. These environments usually demand a high volume of data that must be sent only to those users who needs the information [Greenhalgh, Benford 1997].

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

Dynamic and isometric protocols of knee extension: Effect of fatigue on the EMG signal

The objective of this study was to analyze the electromyographic (EMG) signal behavior of rectus femoris (RF), vastus medialis (VM), vastus lateralis (VL) and biceps femoris (caput longum) (BFCL) from nine women during fatiguing dynamic and isometric knee extensions tests and to determine their EMGFT (Electromyographic Fatigue Threshold). Surface electrodes, biological signal acquisition module, analogical-digital converter board and specific software were used. The RMS (Root Mean Square) values obtained from concentric phase (80 to 30 degrees) of the dynamic knee extension andfrom isometric contraction were correlated with time on each load by linear regression analysis. The respective slopes were correlated with the correspondent load to determine the EMGFT. Force (Kgf) and median frequency - MF (Hz) obtained during MIVC (Maximal Isometric Voluntary Contraction) performed before and after the fatiguing tests were calculated in Matlab environment. The results demonstrated that the endurance time decreases with higher loads the EMG amplitude increase with time and was greater at higher loads, between muscles in dynamic exercise the RF and VL showed higher slopes, and in isometric exercise the VL showed the same behavior The EMGFT values were similar in both exercises; the force values predominantly decreased after fatiguing tests; however the MF only decreased after some loads. The protocols proposed allowed standardizing protocols at least to induce the fatigue process and to determine the EMGFT as an endurance indicative, which may be used to evaluate the effectiveness of rehabilitative or training interventions indicated to reduce muscle weakness and fatigue.

Recurrent neural network for induction motor speed estimation in industry applications

Many electronic drivers for the induction motor control are based on sensorless technologies. The proposal of this work Is to present an alternative approach of speed estimation, from transient to steady state, using artificial neural networks. The inputs of the network are the RMS voltage, current and speed estimated of the induction motor feedback to the input with a delay of n samples. Simulation results are also presented to validate the proposed approach. © 2006 IEEE.

Substitution-newton-Raphson method for the solution of electric network equations

The power flow problem, in transmission networks, has been well solved, for most cases, using Newton-Raphson method (NR) and its decoupled versions. Generally speaking, the solution of a non-linear system of equations refers to two methods: NR and Successive Substitution. The proposal of this paper is to evaluate the potential of the Substitution-Newton-Raphson Method (SNR), which combines both methods, on the solution of the power flow problem. Simulations were performed using a two-bus test network in order to observe the characteristics of these methods. It was verified that the NR is faster than SNR, in terms of convergence, considering non-stressed scenarios. For those cases where the power flow in the network is closed to the limits (stressed system), the SNR converges faster. This paper presents the power flow formulation of the SNR and describes its potential for its application in special cases such as stressed scenarios. © 2006 IEEE.

Branch and bound algorithm for transmission network expansion planning using DC model

This paper presents an algorithm to solve the network transmission system expansion planning problem using the DC model which is a mixed non-linear integer programming problem. The major feature of this work is the use of a Branch-and-Bound (B&B) algorithm to directly solve mixed non-linear integer problems. An efficient interior point method is used to solve the non-linear programming problem at each node of the B&B tree. Tests with several known systems are presented to illustrate the performance of the proposed method. ©2007 IEEE.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

HW/SW for an intelligent transducer network based on IEEE 1451 standard

This work describes a hardware/software co-design system development, named IEEE 1451 platform, to be used in process automation. This platform intends to make easier the implementation of IEEE standards 1451.0, 1451.1, 1451.2 and 1451.5. The hardware was built using NIOS II processor resources on Alteras Cyclone II FPGA. The software was done using Java technology and C/C++ for the processors programming. This HW/SW system implements the IEEE 1451 based on a control module and supervisory software for industrial automation. © 2011 Elsevier B.V.

Transmission network expansion planning considering phase-shifter transformers

This paper presents a novel mathematical model for the transmission network expansion planning problem. Main idea is to consider phase-shifter (PS) transformers as a new element of the transmission system expansion together with other traditional components such as transmission lines and conventional transformers. In this way, PS are added in order to redistribute active power flows in the system and, consequently, to diminish the total investment costs due to new transmission lines. Proposed mathematical model presents the structure of a mixed-integer nonlinear programming (MINLP) problem and is based on the standard DC model. In this paper, there is also applied a specialized genetic algorithm aimed at optimizing the allocation of candidate components in the network. Results obtained from computational simulations carried out with IEEE-24 bus system show an outstanding performance of the proposed methodology and model, indicating the technical viability of using these nonconventional devices during the planning process. Copyright © 2012 Celso T. Miasaki et al.