Ultrasound has been proven to be a valid tool for ranging, especially in water. In this paper, we design a high-resolution ultrasonic ranging system that uses a thin laser beam as an ultrasonic sensor. The laser sensing provides a noncontact method for ultrasound detection based on acousto-optic diffraction. Unlike conventional methods, the ultrasound transmitted from the transducer is recorded as the reference signal when it first passes through the laser. It can be used to improve the accuracy and resolution of the time-of-flight (TOF) by a cross-correlation method. Transducers with a central frequency of 1 MHz and diameters of 20 mm and 28 mm are used in the experiment. Five targets and a test piece are used to evaluate the ranging performance. The sound velocity is measured by the sound velocity profiler (SVP). The repeatability error of TOF is less than 4 ns, and the theoretical resolution of TOF is 0.4 ns. The results show a measurement resolution within one-tenth of the wavelength of ultrasound and an accuracy better than 0.3 mm for targets at a distance up to 0.8 m. The proposed system has potential applications in underwater ranging and thickness detection.

Polar metal–organic frameworks (MOFs) have potential applications due to the fact that their organic linkers can be conveniently modified, which endows them with different properties such as non-linear optical, piezoelectric, pyroelectric, and ferroelectric performance. In the present study, an MIL-53(Cr) MOF material was prepared with a polar organic ligand including a fluorine group. In addition, a polar MOF material can be produced under continuous flow conditions with a space time yield (STY) of 7188 kg m⁻³ day⁻¹. Furthermore, a response surface methodology (RSM) was used to optimize the process conditions with the Box–Behnken design. Moreover, the piezoelectric properties of MIL-53(Cr)-F nanocrystals were probed using dual AC resonance tracking piezo-response force microscopy (PFM) and piezoresponse force spectroscopy (PFS). A PFM amplitude of 300–400 pm and a maximum amplitude of 1051 pm in a butterfly loop were observed indicating its piezoelectricity and ferroelectricity. The MIL-53(Cr)-F nanocrystals display second harmonic generation activity, indicating their polar organization. Therefore, MIL-53(Cr) with a polar linker has the potential to be used as a promising piezoelectric material, which can be produced at a large scale by continuous-flow chemistry.

In the automotive remanufacturing movement, the inspection of the corrosion defects on the engine cylinder cavity is a key and difficult problem. In this article, based on the ultrasonic phased array technology and the radial basis function neural network–genetic algorithm model, a new quantitative analysis method is proposed to estimate the size of the pit defects on the automobile engine cylinder cavity. Echo signals from the small pit defects with different sizes are acquired by an ultrasonic phased array transducer. According to the ultrasonic signal characteristics, the feature vectors are extracted using wavelet packet, fractal technology, peak amplitude method, and some routine extract methods. The radial basis function neural network–genetic algorithm model is investigated for the quantitative analysis of the pit defects, which can obtain an optimal quantitative model. The results show that the proposed model is effective in the corrosion estimation work.

In this paper, we theoretically and experimentally analyze the frequency-comb interferometry at 518 nm in the underwater environment, which we use to measure the underwater distance with high accuracy and precision. In the time domain, we analyze the principle of pulse cross correlation. The interferograms can be obtained in the vicinity of N∙l_pp, where N is an integer and l_pp is the pulse-to-pulse length. Due to the strong dispersion of water, the pulse can be broadened as the distance increases. The distance can be measured via the peak position of the interferograms. The experimental results show a difference within 100 μm at 8 m range, compared with the reference values. In the frequency domain, we analyze the principle of dispersive interferometry. The spectrograms can be observed near the location of N∙l_pp, due to the low resolution of the optical spectrum analyzer. Because of the strong dispersion of water, the modulation frequency of the spectrogram is not constant. A balanced wavelength will exist with the widest fringe, at which the group optical path difference between the reference and measurement arm is equal to N∙l_pp. The position of the widest fringe can be used to measure the distance. Compared with the reference values, the experimental results indicate a difference within 100 μm at 8 m range.

In the practical applications of computed tomography imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scanning conditions. The noisy incomplete projection data results in the ill-posedness of the inverse problems. Based on the observation that the low-resolution reconstruction problem has better numerical stability, we propose a novel low-resolution image prior based CT reconstruction model for limited-angle reconstruction. More specifically, we build up a low-resolution reconstruction problem on the down-sampled projection data, and use the reconstructed low-resolution image as prior knowledge for the high-resolution limited-angle CT problem. The constrained minimization problem is then solved by the alternating direction method with all sub-minimization problems approximated by the convolutional neural networks. Numerical experiments demonstrate that our double-resolution network outperforms both the variational method and popular learning-based reconstruction methods on noisy limited-angle reconstruction problems.

Machine learning-based defect identification has emerged as a promising solution to improving the defect accuracy of the aero-engine blade. This solution adopts machine learning classifiers to classify the types of defects. These classifiers are trained to use features collected in ultrasonic echo signals. However, the current studies show the potential number of features, such as statistic values, for identifying defect reaches a number more than that offered by an ultrasonic echo signal. This necessitates multiple acquisitions of echo signal and increases manual effort, and the feature obtained from feature selection is sensitive to the characteristic of the classifier, which further increases the uncertainty of the classifier result. This paper proposes an ensemble learning technique that is only based on few features obtained from an echo signal and still achieves a high accuracy of defect identification as that in traditional machine learning, eliminating the need for multiple acquisitions of the echo signal. To this end, we apply two well-known ensemble learning classifiers and simultaneously compare three widely used machine learning models on defect identification of blades. The result shows that the proposed ensemble learning models outperform machine learning-based models with an equal number of features. In addition, the two-feature-based ensemble learning model reaches an accuracy close to that of multiple statistic features-based machine learning models, where features are obtained from multiple collections of the signal.