Model-driven terahertz image reconstruction method for debonding defects in thermal barrier coatings.

A terahertz imaging system is considered to be an effective method to study the thermal barrier coating defects in gas turbine engines. However, due to the influence of the system hardware and terahertz wavelength, the imaging system has slow acquisition efficiency, low image resolution, and serious edge blur, which cannot meet the demand for defect detection. To overcome the above defects, a model-driven terahertz image reconstruction method is proposed, which uses simulation data to build datasets, reduces the dependence on experimental data, and has a good reconstruction effect on experimental images. A fusion loss function based on the edge intensity was designed to optimize the edge effect of reconstructed images. Compared with the bicubic, SRCNN, and VDSR methods, the proposed method can achieve better results in terms of visual and evaluation indices for the reduced terahertz images. It is proved that this method can effectively restore the defect contour in the terahertz image, sharpen the edge of the image, and improve the image quality. It has a good application value in the industry.

Rapid detection of trace formaldehyde in food based on surface-enhanced Raman scattering coupled with assembled purge trap.

It has been remained a challenge to detect trace formaldehyde in complex samples, such as rice flour and duck blood products. In this study, a purge-trap device was designed and used for volatile target detection, which avoided interference adsorptions on enhanced particle surfaces during subsequent surface-enhanced Raman spectroscopy (SERS) analysis. The device produced a low detection limit for formaldehyde of 1 × 10-4 µg/mL in the concentration ranges of 4 × 10-3-4 µg/mL and 1 × 10-4-3 × 10-3 µg/mL. In the process of the detection of duck blood and rice flour, partial least squares regression (PLSR) was adopted for sample analysis. The formaldehyde concentration was calculated and compared to the actual value from the above model with R2 of 0.97, which indicated high accuracy and stability. These results suggested that the proposed method was reliable and suitable for rapid analysis of trace formaldehyde in real products.

Two-Time Scale Virtual Sensor Design for Vibration Observation of a Translational Flexible-Link Manipulator Based on Singular Perturbation and Differential Games.

Effective feedback control requires all state variable information of the system. However, in the translational flexible-link manipulator (TFM) system, it is unrealistic to measure the vibration signals and their time derivative of any points of the TFM by infinite sensors. With the rigid-flexible coupling between the global motion of the rigid base and the elastic vibration of the flexible-link manipulator considered, a two-time scale virtual sensor, which includes the speed observer and the vibration observer, is designed to achieve the estimation for the vibration signals and their time derivative of the TFM, as well as the speed observer and the vibration observer are separately designed for the slow and fast subsystems, which are decomposed from the dynamic model of the TFM by the singular perturbation. Additionally, based on the linear-quadratic differential games, the observer gains of the two-time scale virtual sensor are optimized, which aims to minimize the estimation error while keeping the observer stable. Finally, the numerical calculation and experiment verify the efficiency of the designed two-time scale virtual sensor.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique.

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.

A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN.

In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.

Bis[5-(pyridin-2-yl-κN)tetra-zolido-κN (1)]copper(II).

In the title complex, [Cu(C6H4N5)2], the Cu(II) ion lies on an inversion center and is coordinated by two chelating 5-(pyridin-2-yl)tetra-zolide ligands in a slightly distorted square-planar coordination geometry. In the crystal, π-π stacking inter-actions, with centroid-centroid distances in the range 3.4301 (14)-3.4387 (13) Å, link the complex mol-ecules along [101].

[Identification and classification of textiles based on terahertz time domain spectroscopy].

A method to discriminate textiles was proposed based on terahertz time-domain spectroscopy (THz-TDS) and clustering analysis, and some typical cotton textiles were investigated to prove its feasibility. Their time domain waveforms were measured using THz-TDS system and then their absorption spectra were obtained. Principal component analysis (PCA) was applied to extract features of the data, and then Mahalanobis distance discriminant method was employed to classify these materials. The results show that this method can classify these five textiles accurately. It indicates that the method to classify textiles is feasible which combines PCA and Mahalanobis distance discriminant method based on their THz absorption spectra. The proposed method has a potential for identifying textiles of similar composition.