Construção de uma câmara para monitoramento in situ do processo de secagem de geis e sólidos porosos

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

Effect of maltodextrin on the freezing point and thermal conductivity of uvaia pulp (Eugenia piriformis Cambess)

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

Dressing monitoring with a digital signal processor

Grinding is a finishing process in machining operations, and the topology of the grinding tool is responsible for producing the desired result on the surface of the machined material The tool topology is modeled in the dressing process and precision is therefore extremely important This study presents a solution in the monitoring of the dressing process, using a digital signal processor (DSP) operating in real time to detect the optimal dressing moment To confirm the monitoring efficiency by DSP, the results were compared with those of a data acquisition system (DAQ) and offline processing The method employed here consisted of analyzing the acoustic emission and electrical power signal by applying the DPO and DPKS parameters The analysis of the results allowed us to conclude that the application of the DPO and DPKS parameters can be substituted by processing of the mean acoustic emission signal, thus reducing the computational effort

Image registration and image interpolation in volume rendering of cross sections

We outline a method for registration of images of cross sections using the concepts of The Generalized Hough Transform (GHT). The approach may be useful in situations where automation should be a concern. To overcome known problems of noise of traditional GHT we have implemented a slight modified version of the basic algorithm. The modification consists of eliminating points of no interest in the process before the application of the accumulation step of the algorithm. This procedure minimizes the amount of accumulation points while reducing the probability of appearing of spurious peaks. Also, we apply image warping techniques to interpolate images among cross sections. This is needed where the distance of samples between sections is too large. Then it is suggested that the step of registration with GHT can help the interpolation automation by simplifying the correspondence between points of images. Some results are shown.

Georeferenced road extraction and formulation of hypotheses for new road segments

The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operator or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming (DP) algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.

AI automates substation control

An overview is given on the possibility of controlling the status of circuit breakers (CB) in a substations with the use of a knowledge base that relates some of the operation magnitudes, mixing status variables with time variables and fuzzy sets. It is shown that even when all the magnitudes to be controlled cannot be included in the analysis, it is possible to control the desired status while supervising some important magnitudes as the voltage, power factor, and harmonic distortion, as well as the present status.

PMUs optimized allocation using a tabu search algorithm for fault location in electric power distribution system

Phasor Measurement Units (PMUs) optimized allocation allows control, monitoring and accurate operation of electric power distribution systems, improving reliability and service quality. Good quality and considerable results are obtained for transmission systems using fault location techniques based on voltage measurements. Based on these techniques and performing PMUs optimized allocation it is possible to develop an electric power distribution system fault locator, which provides accurate results. The PMUs allocation problem presents combinatorial features related to devices number that can be allocated, and also probably places for allocation. Tabu search algorithm is the proposed technique to carry out PMUs allocation. This technique applied in a 141 buses real-life distribution urban feeder improved significantly the fault location results. © 2004 IEEE.

Using cubic splines to mitigate systematic errors in GPS relative positioning

Systematic errors can have a significant effect on GPS observable. In medium and long baselines the major systematic error source are the ionosphere and troposphere refraction and the GPS satellites orbit errors. But, in short baselines, the multipath is more relevant. These errors degrade the accuracy of the positioning accomplished by GPS. So, this is a critical problem for high precision GPS positioning applications. Recently, a method has been suggested to mitigate these errors: the semiparametric model and the penalised least squares technique. It uses a natural cubic spline to model the errors as a function which varies smoothly in time. The systematic errors functions, ambiguities and station coordinates, are estimated simultaneously. As a result, the ambiguities and the station coordinates are estimated with better reliability and accuracy than the conventional least square method.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Search for associated Higgs boson production WH→WWW *→l±νl′±ν′+X in pp̄ collisions at s=1.96TeV

We present a search for associated Higgs boson production in the process pp̄→WH→WWW*→l±νl′±ν′ +X in final states containing two like-sign isolated electrons or muons (e±e±, e±μ±, or μ±μ±). The search is based on D0 run II data samples corresponding to integrated luminosities of 360-380pb-1. No excess is observed over the predicted standard model background. We set 95% C.L. upper limits on σ(pp̄→WH) ×Br(H→WW*) between 3.2 and 2.8 pb for Higgs boson masses from 115 to 175 GeV. © 2006 The American Physical Society.

An overview of the microstructures present in high-speed steel - Carbides crystallography

The aim of the work was to prepare an overview about the microstructures present in high-speed steel, focused on the crystallography of the carbides. High-speed steels are currently obtained by casting, powder metallurgy and more recently spray forming. High-speed steels have a high hardness resulting from a microstructure, which consists of a steel matrix (martensite and ferrite), in which embedded carbides of different crystal structure, chemical composition, morphology and size, exist. These carbides are commonly named MxC, where M represents one or more metallic atoms. These carbides can be identified by X-ray diffraction considering M as a unique metallic atom. In this work, it is discussed, in basis of the first principles of physics crystallography, the validation of this identification when it is considered that other atoms in the structure are substitutional. Further, it is discussed some requirements for data acquisition that allows the Rietveld refinement to be applied on carbide crystallography and phase amount determination.

Freezing point and thermal conductivity of tropical fruit pulps: Mango and papaya

The freezing point depression of mango and papaya pulps was measured by using a simple apparatus, consisting of two major sections: a freezing vessel and a data acquisition system. The thermal conductivity of both pulps as a function of frozen water fraction and temperature was also investigated by using a coaxial dual-cylinder apparatus. Thermal conductivity above the initial freezing point was well fitted by polynomial equations. Below the freezing point, the thermal conductivity was strongly affected by both the frozen water fraction and temperature. Simple equations in terms of frozen water fraction and temperature could be fitted to the experimental data of freezing point depression and thermal conductivity.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Search for scalar neutrino superpartners in e+mu final states in p(p)over-bar collisions at root s=1.96 TeV

We report a search for R-parity-violating production and decay of sneutrino particles in the eμ final state with 1.04±0.06fb-1 of data collected with the D0 detector at the Fermilab Tevatron Collider in 2002-2006. Good agreement between the data and the standard model prediction is observed. With no evidence for new physics, we set limits on the R-parity-violating couplings λ311′ and λ312 as a function of the sneutrino mass. © 2008 The American Physical Society.

Search for neutral higgs bosons in Multi-b-Jet events in pp̄ collisions at s=1.96TeV

Data recorded by the D0 experiment at the Fermilab Tevatron Collider are analyzed to search for neutral Higgs bosons produced in association with b quarks. This production mode can be enhanced in the minimal supersymmetric standard model (MSSM). The search is performed in the three b quark channel using multijet triggered events corresponding to an integrated luminosity of 1fb-1. No statistically significant excess of events with respect to the predicted background is observed and limits are set in the MSSM parameter space. © 2008 The American Physical Society.