It is difficult to measure elastic modulus simply and accurately in the testing of mechanical properties of materials. Combined with static tensile method, this paper presents a method for measuring elastic modulus of materials based on air-coupled ultrasonic nondestructive testing. Firstly, the 1–3 piezoelectric composite material and the matching material of low acoustic impedance are self-made, and 400 kHz air-coupled ultrasonic transducer is fabricated. Then, the performance of the transducer is tested, and the insertion loss and bandwidth of −6 dB are −33.5 dB and 23.4%, respectively. Compared with the traditional instrument for measuring elastic modulus, the measurement of elastic modulus of carbon steel rod material is realized in this paper, and the measured results are in agreement with the accepted value. In addition, from the angle of relative uncertainty, how to reduce the measurement error by improving the device is analyzed. It can be shown that the method has high linearity, high symmetry, and good stability and repeatability. This paper provides a new way for the selection and design of measuring instrument components.

To get source azimuth from microphone observation angle of view in a complex real environment, this article, on the basis of the analysis of geometric positioning method, established a seven‐element microphone array model and proposed a sound source omnidirectional positioning calibration method based on microphone observation angle. By using a seven‐element array to invert the position and angle of a sound source, the relative time delay value of a pair of microphones on the vertical axis of the coordinate system is used to determine the elevation angle polarity and realize the omnidirectional sound source positioning. The array parameters, sound velocity, array size, horizontal deflection angle, elevation angle, and sound source are analyzed, and the error method is proposed. The sound source data was measured using the microphone array perspective, and a new Cartesian coordinate system was established based on the observation angle of view for omnidirectional positioning calibration of the sound source. The simulation results show that the position error of the method is about 0.01% and the angle error is about 0.005%, with high calibration accuracy. The actual measurement results show that this method can effectively calibrate the sound source azimuth, the error rate of the source coordinates is around 10%, the horizontal declination angle error is less than 5%, and the elevation angle error is less than 8%. Appropriately increasing the spacing of the array will have a better calibration effect in an actual complex experimental environment.

This article presents a thunderstorm prediction system with a three-dimensional (3D) atmospheric electric field (AEF) apparatus (3DAEFA), wherein data source is the high-resolution 3D AEF (3DAEF) values. The system is mainly composed of 3DAEF sensor design and calibration, 3DAEF acquisition and processing, and thunderstorm prediction, localization and imaging. Firstly, a self-made single-axis rotary vane 3DAEFA is employed to measure 3DAEFs. Secondly, a calibration device is developed to calibrate 3DAEF directions while completing intensity calibration. Considering that AEF signal (AEFS) is disturbed by low-frequency noises, full frequency domain AEFS denoising is conducted through Savitzky-Golay (SG) filtering and baseline estimation and denoising with sparsity (BEADS). A prediction model is built, based on bidirectional long short term memory (BiLSTM) network. After inputting denoised AEFS spatial features into the model, which is extracted by convolutional neural network (CNN), a CNN-BiLSTM model is formed. The proposed system is assessed in different weathers. There is a significant improvement in AEFS’s signal-to-noise ratio (SNR) measured by this system, especially in thunderstorm weather, compared with original AEFS (OAEFS). Meanwhile, determining coefficients are more than 95%, showing better prediction effects. The deviation between predicted values and real values is smaller than that of OAEFS, which makes it easier to analyze AEFS characteristics. Comparisons with radar charts demonstrate that the proposed system can effectively predict thunderstorms.

A combination of the sample entropy and the artificial intelligence (AI) would hopefully bring progress to the smart thunderstorm detection. In this article, we establish an entropy-based thunderstorm point charge imaging system with data clustering, in which data are ultrareliable and have low-latency 3-D atmospheric electric field (3DAEF) values. In particular, a high-resolution 3DAEF sensor with the single-axis rotary vane is developed to measure the time sequence signal of the 3DAEF. The signal is first denoised and then decomposed into multiple groups of branch data with the same number of samples. In order to break through the limitations of existing clustering rules, we propose a 3DAEF signal reconstruction method based on entropy intervals. By reconstructing the branch data, multiple time-scale data are formed. Finally, the curve fitting of the data is performed to realize the imaging after clustering the data. Experimental results suggest that the imaging error after the clustering is reduced by about 2.33%. Comparisons with radar charts show that the proposed system can effectively image the point charge moving paths.

As an important factor in fine thunderstorm detections, a multi-time scale thunderstorm monitoring, warning and imaging system is proposed in this paper. The first computing phase involves a decomposition, classification, denoising and reconstruction of the atmospheric electric field signals (AEFSs), collected by a self-made three-dimensional AEF apparatus, based on autocorrelation characteristics and Fuzzy $C$ -Means (FCM). Secondly, FCM classifies the equally divided AEFS components. A scale reconstruction rule is put forward and applied to obtain multi-time scale AEF branch data, according to the component temporal continuity in the same class. A corresponding scale correction strategy is then proposed. Thunderstorm point charge coordinate results are calculated by using branch data, and noise points contained in these results are removed. Finally, the curve fitting of denoised coordinate results is performed to image the point charge moving path. Empirical results confirm that the proposed system effectively warns and images thunderstorms, as well as provides a valid reference for multi-scale thunderstorm monitoring.

Atmospheric electric field signal (AEFS) features can be characterized by their average value, standard deviation, and entropy value. How to mine and fully utilize AEFS features to ensure reliable and efficient thunderstorm detection has not been considered so far. In this paper, based on the stacked autoencoder and extreme gradient boosting model, extracted deep-seated features of AEFS are used to obtain its predicted value (PV). It fuses three regular features plus one PV feature and proposes a thunderstorm moving path (TMP) prediction system with switchable patterns among the applied three AEFS prediction models based on the convolutional neural network and bi-directional long short term memory. This fully considers that a single model is difficult to meet AEFS predictions with different weather attributes. Specifically, AEF data measured by a self-made AEF apparatus are adopted to determine feature values (FVs). According to FV intervals (FVIs) in sunny and thunderstorm weathers, the proportion of each feature satisfying FVIs is taken as the weighting factors of corresponding feature terms. A switchable pattern function with different switching conditions is formed by combining weightings and feature variables. Optimal AEFS prediction models are fixed under the same switching condition and applied to corresponding patterns. Empirical results confirm that the proposed system effectively predicts TMPs, with an average determination coefficient of 95.58%. This is the first study to design switchable patterns to detect thunderstorms from a new perspective of multiple AEFS feature fusion, which provides promising solutions to the refinement and intelligent prediction of thunderstorms.