Abstract:The estimation of heat conduction properties has considerable importance in the characterization of bamboo with respect to its potential use as an alternative construction material. Even though traditional methods such as hot plates have successfully measured thermal parameters, like thermal diffusivity and conductivity in bamboo samples, it is still necessary to transform the cylindrical bamboo specimen into a piece with special geometry and size. This requirement makes this method impractical in applications where several bamboo specimens need to be measured in their original cylindrical shape. This paper presents the estimation of thermo-physical parameters k and ρc p in Guadua angustifolia kunth (Guadua a.k.) bamboo through nonlinear least square optimization and infrared thermography. A sensitivity analysis was carried out to determine how the temperature on the bamboo surface is affected by changes in the convection coefficient h, thermal conductivity k, and volumetric heat capacity ρc p . In spite of the nonlinearity and high correlation in the parameters of the inverse heat conduction problem (IHCP), the estimation of such parameters is robust and consistent with those reported in the literature.
Quantitative Pulsed Phase Thermography (PPT) has been only used to estimate defect parameters such as depth and thermal resistance. Here, we propose a thermal quadrupole based method that extends quantitative pulsed phase thermography. This approach estimates thermal diffusivity by solving a inversion problem based on non-linear squares estimation. This approach is tested with pulsed thermography data acquired from a composite sample. We compare our results with another technique established in time domain. The proposed quantitative analysis with PPT provides estimates of thermal diffusivity close to those obtained with the time domain approach. This estimation requires only the a priori knowledge of sample thickness.
Infrared (IR) images are representations of the world and have natural features like images in the visible spectrum. As such, natural features from infrared images support image quality assessment (IQA).. 1 In this work, we compare the quality of a set of indoor and outdoor IR images reconstructed from measurement functions formed by linear combination of their pixels. The reconstruction methods are: linear discrete cosine transform (DCT) acquisition, DCT augmented with total variation minimization, and compressive sensing scheme. Peak Signal to Noise Ratio (PSNR), three full-reference (FR), and four no-reference (NR) IQA measures compute the qualities of each reconstruction: multi-scale structural similarity (MSSIM), visual information fidelity (VIF), information fidelity criterion (IFC), sharpness identification based on local phase coherence (LPC-SI), blind/referenceless image spatial quality evaluator (BRISQUE), naturalness image quality evaluator (NIQE) and gradient singular value decomposition (GSVD), respectively. Each measure is compared to human scores that were obtained by differential mean opinion score (DMOS) test. We observe that GSVD has the highest correlation coefficients of all NR measures, but all FR have better performance. We use MSSIM to compare the reconstruction methods and we find that CS scheme produces a good-quality IR image, using only 30000 random sub-samples and 1000 DCT coefficients (2%). In contrast, linear DCT provides higher correlation coefficients than CS scheme by using all the pixels of the image and 31000 DCT (47%) coefficients.
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