Zhi-Tao Luo

Bio: Zhi-Tao Luo is an academic researcher from Southeast University. The author has contributed to research in topics: Materials science & Feature (linguistics). The author has an hindex of 1, co-authored 3 publications receiving 1 citations.

Investigation of total diffuse-photon-density-wave field in semi-infinite turbid media based on the extrapolated Beer-Lambert law

Abstract: The accurate description of the total diffuse-photon-density-wave field inside turbid media, especially in the near-field region, is extremely critical but challenging for many decades. Here, the total diffuse-photon-density-wave field of semi-infinite turbid media was calculated by the third-order simplified spherical harmonics approximation (SP3) and compared with Monte Carlo simulations. To improve the SP3 approximation, the extrapolated Beer–Lambert law model considering the contribution of the coherent-photon-density-wave in the near-field region was proposed and implemented by Levenberg–Marquardt and universal global optimization methods. Last, we demonstrated the superiority of the proposed model over the existing model in fitting the accuracy and applicable source–detector distance range. The high accuracy and simplicity of the proposed model would be extremely helpful for biomedical applications involving photothermal radiometry, and rapidly determining optical properties of media, along with photoacoustic imaging and photodynamic therapy.

Graph-in-Graph Convolutional Network for Ultrasonic Guided Wave-Based Damage Detection and Localization

Abstract: Interpretation of guided wave signals is a central challenge for ultrasonic guided wave-based damage detection and localization technology. Because of the complexity of the guided waves that are scattered from structural damage, existing guided wave-based damage detection methods cannot be used to extract the relationship information hidden in the guided waves for use in damage detection and localization. A graph-in-graph convolutional network is thus proposed for guided wave-based damage detection and localization that constructs spatial–temporal feature representations of the guided wave signals and interconnects them into a global graph to indicate the inherent differences among these signals. By converting the guided wave characteristics into structural and topological information in non-Euclidean space, the proposed method correlates the global graph with the damage location directly and achieves greater damage detection accuracy with fewer training data. Validations are performed using two different experimental datasets, which were collected from aluminum plates and a composite laminate. The results indicate that the proposed method achieves superior performance with high accuracy and stability for even limited and imbalanced datasets acquired with only three transducers.

Advanced orthogonal frequency and phase modulated waveform for contrast-enhanced photothermal wave radar thermography

Abstract: Thermal wave radar (TWR) thermography is a high-efficient nondestructive testing technique to increase the signal-to-noise ratio (SNR) and to enhance target detection capability. However, the detection of subsurface defects, especially small-size defects, usually requires a distinctively high SNR and depth resolvability. This paper proposed an orthogonal phase-coded linear frequency modulated (OPCLFM) excitation waveform, which has significantly improved the SNR and depth resolvability of TWR compared to the LFM waveform. The pulse compression quality of the OPCLFM waveform was initially evaluated through a 1D thermal wave analytical model of carbon fiber reinforced polymer (CFRP) laminate. Results show that the OPCLFM waveform combined with the Kaiser window function compresses the largest sidelobe at least by 18.39 dB compared to the LFM waveform. Furthermore, the superior depth resolvability performance of the OPCLFM waveform was also validated by 3D finite element simulation. Finally, the effect of thermal conductivity on the depth resolvability performance of the OPCLFM waveform was evaluated quantitatively by a delaminated CFRP laminate.

Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography

Abstract: In this article, we present a smooth low-rank tensor completion (SLRTC) based reconstruction algorithm to recover raw thermal image sequences from highly randomly undersampled or small numbers of available thermographic data. The presented algorithm is also fused with a temporal interpolation algorithm (to produce the SLRTCTI algorithm) to complement high frame-rate thermal image sequences with notably enhanced thermal contrast. Pulsed and lock-in thermographic data are obtained for subsurface defects in carbon fiber reinforced polymer (CFRP) to demonstrate the performance of the algorithm, and it is shown that the algorithm is data-driven and is independent of the excitation form. The algorithm enables the maximum available frame rates of thermal infrared cameras to be increased by at least ten times. To further enhance the visibility of the CFRP defects in the results reconstructed using the SLRTC algorithm, fast randomized sparse principal component thermography (FRSPCT) and 2-D principal component thermography (TDPCT) are also proposed. Results show that TDPCT remarkably enhances the thermal contrast between the defective and intact regions under highly undersampled data conditions. In addition, FRSPCT provides more easily interpretable detection results and highlights the hidden details of irregularly-shaped abnormal defects.

Advanced orthogonal frequency and phase modulated waveform for contrast-enhanced photothermal wave radar thermography

Abstract: Thermal wave radar (TWR) thermography is a high-efficient nondestructive testing technique to increase the signal-to-noise ratio (SNR) and to enhance target detection capability. However, the detection of subsurface defects, especially small-size defects, usually requires a distinctively high SNR and depth resolvability. This paper proposed an orthogonal phase-coded linear frequency modulated (OPCLFM) excitation waveform, which has significantly improved the SNR and depth resolvability of TWR compared to the LFM waveform. The pulse compression quality of the OPCLFM waveform was initially evaluated through a 1D thermal wave analytical model of carbon fiber reinforced polymer (CFRP) laminate. Results show that the OPCLFM waveform combined with the Kaiser window function compresses the largest sidelobe at least by 18.39 dB compared to the LFM waveform. Furthermore, the superior depth resolvability performance of the OPCLFM waveform was also validated by 3D finite element simulation. Finally, the effect of thermal conductivity on the depth resolvability performance of the OPCLFM waveform was evaluated quantitatively by a delaminated CFRP laminate.

Applicability of Beer's law in particulate system from random to regular arrangement: A numerical evaluation

Abstract: Beer's law is the foundation of the classic radiative transfer theory. Recently, many studies have pointed out the existence of non-Beerian transport behavior in spatially correlated and statistically non-homogeneous and anisotropic particulate systems. However, there still lacks a comprehensive quantitative evalution of the applicability of Beer's law in particulate medium from random to regular arrangement. In this work, a numerical approach is proposed to quantitatively analyze and determine the applicability of Beer's law in particulate systems from regular arrangement to random arrangement. A simple approach for characterizing structural randomness of particulate system is presented. The results show that the non-Beerian behaviors exist in statistically isotropic and homogeneous particulate media. The structural randomness has a significant influence on the transmission distribution and hence the applicability of Beer's law in particulate systems. Quantitative dependent relation of validity of Beer's law on structural randomness in particulate system is obtained. Both particle spacing and structural randomness have significant impact on the applicability of Beer's law. This work is helpful for the understanding of non-Beerian radiative transfer in particulate media and guiding related engineering applications of radiative transfer theory.