Luiz Alberto Luz de Almeida

Parameter identification of thermoeletric modules using particle swarm optimization

This paper presents a methodology to estimate thermoelectric module (TEM) internal parameters based on particle swarm optimization (PSO) algorithm. To obtain the correct TEM representation, it is necessary a proper model identification procedure to represent the TEM operation, both in DC and other relevant frequencies. Classical methods for linear parameter estimation are not suitable for the nonlinear TEM characteristics of the proposed model. We devise a model with twenty-one parameters, which represent parts of the two TEMs employed, including top, lower and middle layers and heat-sinks. The TEM is excited using an electrical current signal with power spectral density of a white noise, and the temperature response is adopted as output for the PSO algorithm to make the estimation. For numerical stability and proper estimation, the white noise excitation is filtered before, to obtain a dynamically persistent signal with high and low frequencies components. Simulation results show the effectiveness of the PSO in TEM parameters estimation.

METHOD THRESHOLDING AUTOMATIC FOR SOMATIC CELL COUNT IN MICROSCOPIC IMAGES

The objective of this work was to present a segmentation method and somatic cell count of bovine milk for images obtained directly from the optical microscope through a proposal for automatic threshold. For purposes of verifying efficiency of the technique, proposed thresholding method was compared with two thresholding methods known in the literature. The correlation between the manual reference counting and thresholding methods tested was 0.98, 0.71 and 0.12, respectively. The proposed method acceptable correlation demonstrating efficiency of this algorithm for thresholding and somatic cell count and other similar applications. Key-words: microscope; Rosin thresholding; Otsu thresholding.

Parameter estimation of nonlinear thermoelectric structures using particle swarm optimization

Abstract The purpose of this investigation is motivated mainly for thermal energy harvesting devices and temperature feedback controllers interacting with electronic circuits. Mostly intended to design Linear Quadratic Gaussian (LQG) type controllers, suitable for uncertain dynamical systems, in which not all state variables are measured and available for proper feedback. We present a methodology for modeling and estimation of several internal parameters for a proposed thermal characterization apparatus that employs thermoelectric modules (TEMs). Repeated random vector sampling, similar to Monte Carlo method, is combined with particle swarm optimization (PSO) algorithm for parameter estimation. For the intended applications, is mandatory to drive apparatus that have embedded TEMs, not only using direct current powering, as usually done in literature, but also with alternate current signals over a large range of relevant frequencies. For exciting the many nonlinear and linear states during the identification procedure, a single embedded TEM is injected with a proper random electrical current signal having power spectral density of a band-limited white noise. Sensitivity to both initial conditions and different sets of random excitation, brings uncertainty in the estimated 21 parameters of our mechanical apparatus with two embedded TEMs and their corresponding dynamics. Simulation are presented showing the effectiveness of the proposed estimation technique, with convergence performance and parameter statistical distribution over a set of uncorrelated random current vector excitation.

Optical properties of thin VO 2 -films at the phase transition: Design aspects for a fiber-optic thermometer

The pronounced variation of the optical reflectance R of nanometer thin VO2 films with temperature T at the phase transition recently has triggered interest in the development of compact thermo-optical sensors that utilize optical fiber technology. Its immunity against electromagnetic interference effects makes this approach suited for an optical detection of local areas with increased temperature, i.e. so called hot spots, inside of high power transformers. Optical reflectance of thin VO2-films as function of T throughout the hysteretic phase transition is reported in this work. A modified version of the Preisach hysteresis model is utilized, and combined with experimental optical data. Suited VO2 film thickness values and wavelengths range for optimum sensor operation can be extracted. The achievable temperature range, as confirmed by experimental observations, is consistent with requirements that apply for power transformers of the first group, according to NBR 5416 (ABNT).