In this study, an algorithm for denoising ultrasound echo signals in industrial settings is proposed to address the problem of high noise and low signal-to-noise ratio. The algorithm combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information entropy (MIE), and wavelet threshold denoising to ensure effectiveness given the unique structure of ultrasound echo signals. Initially, CEEMDAN is used to decompose the signal into intrinsic mode function (IMFs) and residual signals. The MIE is then used to determine the correlation of neighboring IMF signals, which are then divided into a noise- and a signal-dominated part. Finally, using wavelet thresholding, noise is suppressed in the signal-dominant part, and the resulting denoised signal is reconstructed using the residual signal. The performance of the algorithm is verified through simulations and physical experiments, and the results show that it is superior to traditional signal denoising methods.

In recent years, master–slave vascular robots have been developed to address the problem of radiation exposure during vascular interventions for surgeons. However, the single visual feedback reduces surgeon immersion and transparency of the system. In this work, we have developed a haptic interface based on the magnetorheological fluid (MRF) on the master side. The haptic interface can provide passive feedback force with high force fidelity and low inertia. Additionally, the manipulation of the master device does not change the operating posture of traditional surgery, which allows the surgeon to better adapt to the robotic system. For the slave robot, the catheter and guidewire can be navigated simultaneously which allows the two degrees of action on the catheter and axial action of a guidewire. The resistance force of the catheter navigation is measured and reflected to the user through the master haptic interface. To verify the proposed master–slave robotic system, the evaluation experiments are carried out in vitro, and the effectiveness of the system was demonstrated experimentally.

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.

An apparatus for the absolute determination of the sound speed in water based on the time-of-flight technique is described. The time measurement is realized by hardware circuits and the distance measurement by a double-beam plane-mirror interferometer. A highly accurate time chip, with a resolution of approximately 90 ps, is employed for time measurements. The acoustic path length is adjustable and can be measured directly. Two transducers are used for transmitting and receiving ultrasonic signals without reflection. The transducers are immersed in a thermostatic vessel that maintains bath temperature with high stability. The speed of sound in pure water was measured at ambient pressure and at the temperatures 308 K, 303 K, 298 K, and 293 K. The achieved measurement uncertainties are 2 mK for temperature and 0.045 m/s for speed of sound. The results are compared to data from the literature, equation of state models, and measurements by two commercial sensors in the same experiment, showing excellent agreement among them.

In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key points feature aggregation, we propose an NAM-STGCN model that incorporates a normalized attention mechanism. By using the activation function PReLU to optimize the model structure, the improved ST-GCN model can more effectively extract skeletal key points action features in the spatio-temporal dimension for the purposes of abnormal behavior recognition. The experimental results show that our optimized model achieves a 96.72% accuracy for recognition on the self-built dataset, which is 4.92% better than the original model; the model loss value converges below 0.2. Tests were performed on the KTH and Le2i datasets, which are both better than typical classification recognition networks. The model can precisely identify abnormal human behaviors, facilitating the detection of abnormalities and rescue in a timely manner and offering novel ideas for smart site construction.

Measuring sound velocity plays an important role in ocean exploration and perception technology. In this paper, we propose a method to directly measure underwater sound velocity using dual-comb lasers and matched filtering. Among them, the acousto-optic effect between the frequency comb and ultrasonic waves is applied to calibrate the time of flight. The dual-comb interferometry technique is utilized to determine the underwater sound velocity. Furthermore, matched filtering is applied to enhance the signal-to-noise ratio of the signal. Experimental results demonstrate that the method achieves a kHz-scale sound velocity measuring rate, and enjoys an uncertainty of better than 0.037 m/s for a single measurement. Moreover, compared with commercial sound velocity profilers, the method exhibits a standard deviation of 0.0154 m/s during long-term measurements.