10 resultados para Field monitoring

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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

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

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Drug delivery systems based on natural polysaccharides, such as chitosan (CS) and pectin (PC), rather than on synthetic polymers, have been widely studied. Some reasons for that are low toxicity and costs and high biodegradability of the formers. A multiparticulate system based on CS and PC was developed in our laboratories, including the addition of an enteric polymer, cellulose acetate phtalate (CAP). Such improvement promoted stronger gastric and enteric resistances, as assessed in vitro, making the systems more selective to enzymatic degradation in the colon. Although in vitro dissolution tests can simulate some properties concerning the gastrointestinal transit (GT), collaborating to characterize the systems behavior in the biological fluids, frequently they do not result in satisfactory in vitro/in vivo correlations. The objective of this work was to follow in vivo the GT of the particles developed by means of AC biosusceptometry (ACB), a non-invasive and of low cost methodology. The particles containing ferrite in powder form were prepared by complex coacervation using an ideal 3:1:1 mass ratio for PC:CS:CAP. The magnetic particles were administered to healthy volunteers by oral route. The GT was monitored by using multi-sensor ACB system and the signal acquisition was performed every IS min until the colonic region was reached. By means of ACB technique, it was possible to acquiring images generated by the magnetic particles within the whole gastrointestinal tract including the colonic region. Variable particles transit times were observed among the volunteers, but without interference on the mapping of the particles until the colonic region. The particles were able to produce magnetic field strong enough to generate signals adequate for mapping the particles. The results suggest that integral particles reached the colon, after they resisted against gastric and enteric media. Studies associating transit time and in vivo drug release are in development in order to confirm the efficiency of the systems.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.

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This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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