Relação entre a tensão aplicada e a sensação de desconforto nos músculos isquiotibiais durante o alongamento

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

In-process grinding monitoring by acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. Many statistics have shown effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS.

Fault section estimation in electric power systems using an optimization immune algorithm

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

Sistema de aquisição de dados para estudos de transiente de pressão e detecção de vazamentos em oleodutos

In February 2011, the National Agency of Petroleum, Natural Gas and Biofuels (ANP) has published a new Technical Rules for Handling Land Pipeline Petroleum and Natural Gas Derivatives (RTDT). Among other things, the RTDT made compulsory the use of monitoring systems and leak detection in all onshore pipelines in the country. This document provides a study on the method for detection of transient pressure. The study was conducted on a industrial duct 16" diameter and 9.8 km long. The pipeline is fully pressurized and carries a multiphase mixture of crude oil, water and natural gas. For the study, was built an infrastructure for data acquisition and validation of detection algorithms. The system was designed with SCADA architecture. Piezoresistive sensors were installed at the ends of the duct and Digital Signal Processors (DSPs) were used for sampling, storage and processing of data. The study was based on simulations of leaks through valves and search for patterns that characterize the occurrence of such phenomena

Pulpal temperature increase with high-speed handpiece, ER : YAG laser and ultrasound tips

The aim of this study was to compare intrapulpal temperature increase produced by high-speed handpiece, Er:YAG laser and CVDentus ultrasound tips during cavity preparation. Thirty bovine mandibular incisors with an enamel/dentin thickness of 4 mm at buccal surface had their roots amputated and were allocated to the following groups (n=10): Group I- high-speed handpiece; Group II- noncontact Er:YAG laser (250 mJ/4Hz); and Group III- CVDentus ultrasouns tips. All devices were used with water cooling. Class V cavities were prepared to a depth of 3.5 mm, measured with a periodontal probe. A type T thermocouple was placed inside the pulp chamber to determine the temperature increase (degrees C), which was recorded by a data acquisition system ADS 2000 IP (Lynx Technology) linked to a notebook computer. Data were analyzed statistically by oneway ANOVA and Tukey's test (p=0.05). The mean temperature rises were: 1.10 degrees C ( 0.56) for Group 1, 0.84 degrees C (0.55) for Group II, and 3.00 degrees C (1.34) for Group III. There were no statistically significant differences (p > 0.05) between Groups I and II, but both of them differed significantly from Group III (p < 0.05). In conclusion, the use of Er:YAG laser and high-speed handpiece for cavity preparation resulted in similar temperature increase. Although ultrasound tips generated significantly higher intrapulpal temperature increase, it remained below the critical value of 5.5 degrees C and may be considered safe for use.

Sincronismo de dispositivos para o georreferenciamento de imagens digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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

Control of thermal damage in grinding by digital signal processing of raw acoustic emission

Grinding process is usually the last finishing process of a precision component in the manufacturing industries. This process is utilized for manufacturing parts of different materials, so it demands results such as low roughness, dimensional and shape error control, optimum tool-life, with minimum cost and time. Damages on the parts are very expensive since the previous processes and the grinding itself are useless when the part is damaged in this stage. This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 e VC131 steels. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. In each test AE data was analyzed off-line, with results compared to inspection of each workpiece for burn and other metallurgical anomaly. A number of statistical signal processing tools have been evaluated.

In-process grinding monitoring through acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding processes. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045 Steel as work material. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system working at 2.5 MHz was used to collect the raw acoustic emission instead of the root mean square value usually employed. Many statistical analyses have shown to be effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm rate (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS. Copyright © 2006 by ABCM.

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.

Lumbo-pelvic stability and electromyography of abdominal muscles in ballet dancers

Evaluating the ability to rectify and maintain lumbar adjustment can contribute toward the understanding of the behavior of abdominal muscles and their participation in the stability of pelvic muscles in dancers during the posterior pelvic tilt and double straight leg lowering tests. Nine healthy volunteers (male and female ballet dancers; age mean: 25.9 ±7.37 years) underwent maximal isometric voluntary contraction (MIVC), isometric voluntary contraction at 50% of MIVC, posterior pelvic tilt (PPT) and double straight leg lowering (DSLL) tests. The tests were carried out in a single day, with 3 repetitions each. During the tests, electromygraphic signals of the rectus abdominis, obliquus internus and obliquus externus were recorded. The signal acquisition system was made up of bipolar surface electrodes, electrogoniometer and an electromechanic device (pressure sensor), which were connected to a signal conditioner module. Root mean square values of each muscle during the DSLL and PPT were converted into percentage of activation of 50% MIVC. Lower back pressure was submitted to the same process. ANOVA with repeated measures was performed, with the level of significance set at p < 0.05. The results revealed that all dancers were able to maintain posterior pelvic tilt and there was trend toward greater activation of the bilateral obliquus internus muscle. In an attempt to keep the pelvic region stabilized during DSLL, there was a greater contribution from the obliquus externus muscle in relation to other abdominal muscles.

Plataforma para medição de forças na região plantar de pacientes normais e hemiplégicos

This work describes an electronic system implementation with two force plates, 24 load cells with strain gages, signal conditioning circuit, interfacing circuit and data acquisition system, designed for measuring plantar force distribution in patients. The system presented linear response, low hysteresis, with determination coefficient of 0.9997, precision better than 0,84% and resolution less than 0.5 N. The Measurements are presented on a computer screen and easier visualization to specialists, mainly physicians, physiotherapists and occupational therapists. Using the system, the distribution of weight in the plantar region of 100 normal subjects and 10 hemiplegic patients was investigated. There were determined the relationship between weight distribution on the right and left forefoot, hemiplegic patients and normal patients, with the implemented system. © 2013 Springer.

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA.