In shallow water, passive sonar usually has great difficulty in discriminating a surface acoustic source from an underwater one. To solve this problem, a supervised machine learning method using only one hydrophone is implemented in this paper. Firstly, simulated training data are generated by a normal mode model KRAKEN with the same environment setup as that in SACLANT 1993 experiment. Secondly, the k-nearest neighbor (kNN) classifiers are trained and evaluated using the scores of precision, recall, F1 and accuracy. Thirdly, the random subspace kNN classifiers are finely trained on three hyperparameters (the number of nearest neighbors, the number of predictors selected at random and the number of learners in the ensemble) to obtain the best model. Fourthly, a deep learning method called ResNet-18 is also applied, and it reaches the best balance between precision and recall, while the accuracies of both simulation and experimental data are all 1.0. Further, data from all 48 hydrophones of the vertical linear array (VLA) are analyzed using the three kinds of machine learning methods (kNN, random subspace kNN and ResNet-18) separately, and the results are compared. It is concluded that the performance of random subspace kNN is the best. Both the simulation and experimental results suggest the feasibility of machine learning as a surface and underwater acoustic source discrimination method even with only a single hydrophone.

Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.

Acoustic agglomeration (AA) is considered to be a promising method for reducing the air pollution caused by fine aerosol particles. Removal efficiency and energy consuming are primary parameters and generally have a conflict with each other for the industry applications. It was proved that removal efficiency is increased with sound intensity and optimal frequency is presented for certain polydisperse aerosol. As a result, a high efficiency and low energy cost removal system was constructed using acoustical resonance. High intensity standing wave is generated by a tube system with abrupt section driven by four loudspeakers. Numerical model of the tube system was built base on the finite element method, and the resonance condition and SPL increase were confirmd. Extensive tests were carried out to investigate the acoustic field in the agglomeration chamber. Removal efficiency of fine particles was tested by the comparison of filter paper mass and particle size distribution at different operating conditions including sound pressure level (SPL), and frequency. The experimental study has demonstrated that agglomeration increases with sound pressure level. Sound pressure level in the agglomeration chamber is between 145 dB and 165 dB from 500 Hz to 2 kHz. The resonance frequency can be predicted with the quarter tube theory. Sound pressure level gain of more than 10 dB is gained at resonance frequency. With the help of high intensity sound waves, fine particles are reduced greatly, and the AA effect is enhanced at high SPL condition. The optimal frequency is 1.1kHz for aerosol generated by coal ash. In the resonace tube, higher resonance frequencies are not the integral multiplies of the first one. As a result, Strong nonlinearity is avoided by the dissonant characteristic and shock wave is not found in the testing results. The mechanism and testing system can be used effectively in industrial processes in the future.

The sintering flue gas in steel production has the characteristics of large discharge capacity, fine size, high temperature, corrosion and fluctuation of working conditions, and is the primary control object of PM_2.5 emission source. Traditional dust removal technologies and equipment are difficult to achieve efficient collection of fine particles. In order to quantitatively study the effect and regularity of high intensity sound waves on fine particles, the online acoustic agglomeration test system was built by using high-power pneumatic source and wave tube with abrupt section. The high intensity standing wave field of 140 to 150 dB was generated in the wide frequency range between 50 Hz and 2 kHz by the acoustic resonance synthesis, and interference from compressed airflow was avoided by the acoustic-flow separation module. Variation of the sintered flue gas PSD was obtained under different sound intensities and frequencies. Data analysis shows that the high concentration condition can greatly reduce the operation time and energy consumption. Under the action of high intensity acoustic waves with a fundamental sound pressure level of 87.3-161.8dB and duration of 3s, the measured PM_2.5 removal efficiency is 16%-92%, and the optimal frequency is near 800Hz. This paper verifies the feasibility of acoustic agglomeration (AA) in the treatment of sintering flue gas, and provides a theoretical reference and technical basis for the development of PM_2.5 real-time emission control device for steel production.

The sonar image has limitations on the physical spatial resolution due to system configuration and underwater environment, which often leads to challenges for underwater targets detection. Here, the deep learning method is applied to enhance the physical spatial resolution of underwater sonar images. Specifically, the U-shaped end-to-end neural network which contains down-sampling and up-sampling parts is proposed to improve the physical spatial resolution limited by the array aperture. The single target and multiple cases are considered separately. In both cases, the normalized loss on the testing sets declines rapidly, and the predicted high-resolution images own great agreement with the ground truth eventually. Further improvements in resolution are focused on, that is, compressing the predicted high-resolution image to its physical spatial resolution limitation. The results show that the trained end-to-end neural network could map high resolution targets to the impulse responses at the same location and amplitude with an uncertain target number. The proposed convolutional neural network approach could give a practical alternative to improve the physical spatial resolution of underwater sonar images.

Hybrid underwater gliders (HUGs) combine buoyancy-driven gliding with propeller-assisted propulsion, offering extended endurance and enhanced mobility for complex underwater missions. However, precise depth control remains challenging due to system uncertainties, environmental disturbances, and inadequate adaptability of conventional control methods. This study proposes a novel optimized line-of-sight active disturbance rejection control (OLOS-ADRC) strategy for HUG depth tracking in the vertical plane. First, an Optimized Line-of-Sight (OLOS) guidance dynamically adjusts the look-ahead distance based on real-time cross-track error and velocity, mitigating error accumulation during path following. Second, a Tangent Sigmoid-based Tracking Differentiator (TSTD) enhances the disturbance estimation capability of the Extended State Observer (ESO) within the Active Disturbance Rejection Control (ADRC) framework, improving robustness against unmodeled dynamics and ocean currents. As a critical step before costly sea trials, this study establishes a high-fidelity simulation environment to validate the proposed method. The comparative experiments under gliding and hybrid propulsion modes demonstrated that OLOS-ADRC has significant advantages: the root mean square error (RMSE) for depth tracking was reduced by 83% compared to traditional ADRC, the root mean square error for pitch angle was decreased by 32%, and the stabilization time was shortened by 14%. This method effectively handles ocean current interference through real-time disturbance compensation, providing a reliable solution for high-precision HUG motion control. The simulation results provide a convincing foundation for future field validation in oceanic environments. Despite these improvements, the study is limited to vertical plane control and simulations; future work will involve full ocean trials and 3D path tracking.