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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new coronavirus causing a worldwide pandemic. Moreover, the best T-shaped configurations were achieved for long single and bifurcated branches, except for configurations with skinny channels, which saw the generation of permanent voids. For the studied conditions, the best T-shaped configuration resulted in a filling time nearly three times lower than that for optimal I-shaped geometry, showing that the complexity of the channels benefited the performance. Interaction between the phases was considered with the volume of fluid method (VOF), and the effect of porous medium resistance in the resin flow was calculated with Darcy’s law. Conservation equations of mass and momentum for modeling of resin/air mixture flow were numerically solved with the finite volume method (FVM). In addition, the I and T-shaped channels were subjected to one and three degrees of freedom (DOF), respectively. The problem was subjected to two constraints (areas of porous mold and empty channels). Geometrical optimization was conducted with the constructal design (CD) and exhaustive search (ES) methods. This numerical study aims to investigate the influence of I and T-shaped empty channels’ geometry on the filling time of resin in a rectangular porous enclosed mold, mimicking the main operating principle of a liquid resin infusion (LRI) process.
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The proposed method is helpful for accurately detecting defects in complex defect topography maps and may help to accelerate defect detection and characterization procedures. Simulated data and experimental captured data from samples prove that the proposed method can effectively reduce the speckle noise of the DHSPI wrapped phase to extract the desired information. Thus, the paper proposes a new method to remove speckle noise in the wrapped phase. Although a related method applied to interferometry for reducing Gaussian noise has been introduced, it is not suitable for application in DHSPI to reduce speckle noise. An effective method for denoising DHSPI wrapped phase based on deep learning is presented in this paper. Distortions in fringe patterns are recorded and observed from the estimated wrapped phase map, but the inevitable electronic speckle noise directly affects the quality of the image and consequently the assessment of defects. The spatial information of structural defects is mostly delivered as local distortions interrupting the smooth distribution of intensity during the phase-shifted formation of fringe patterns. DHSPI is a non-destructive and non-contact imaging method that has been successfully applied to the structural diagnosis of artworks by detecting hidden subsurface defects and quantifying the deformation directly from the surface illuminated by coherent light. This paper presents a new processing method for denoising interferograms obtained by digital holographic speckle pattern interferometry (DHSPI) to serve in the structural diagnosis of artworks.
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To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them. PDF is the official format for papers published in both, html and pdf forms.You may sign up for e-mail alerts to receive table of contents of newly released issues.Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.Therefore, this paper contributed to make the fault injection attack more realistic. Using the proposed system, it is possible to perform the fault injection attack on commercial devices such as a smart card, that are difficult to generate artificial triggers. In this paper, we proposed a novel fault injection system using Input / Output (I/O) signals of target devices as a trigger for relaxing an attacker assumption. These have the disadvantage that an attacker assumptions are strong to perform fault injection attacks against commercial devices. Traditional fault injection systems utilized an artificial trigger close to a target operation.