مجله انجمن مهندسی صوتیات ایران
سال یازدهم شماره 1 (بهار و تابستان 1402)

This paper proposes the kinematics and dynamics modelling of a three degree of freedom platforms, initially. Equations from this modelling are extracted in the form of input-output without any simplification. Given the application of these platforms in the stabilization of acoustic systems on movable systems, linear and nonlinear controllers are designed based on the extracted equations. Since the adaptive fuzzy controller is considered as an effective way for controlling uncertainties in nonlinear systems, a new algorithm is proposed in this paper based on the adaptive direct fuzzy control because of its robust tracking performances, ensured stability, and high-accuracy responses. In the design process of the adaptive direct fuzzy controller, the consistencies of the proposed algorithm and the validity of the control method are confirmed by the stability analysis and simulation results. This control method ensures the stability of the platform when it is subjected to disturbances from its carrier platform. The proposed algorithm is then compared with the proportional-integral-derivative controller. The simulation result shows the superiorities of the proposed approach. In addition, based on the simulation results, the stabilization accuracies of the adaptive fuzzy control method is estimated to be 1000 times higher than that of the proportional-integral-derivative control method.

Accurate and correct identification of vessels moving in the waters from a distance is important in many ways. Having information type of vessels more correct decisions on how to deal with them. The sounds emitted by the vessels can be distinguished from each other and it is possible to identify the vessels by their sound. Therefore, in this study, the vessels have been classified based on their weight and the sound emitted by them. In the present study, the characteristics of each of the sounds recorded the vessels were extracted using the Mel-Frequency Cepstrum Coefficients (MFCC) method, and the ability of the artificial neural network and its hybrids with the Genetic Algorithm (ANN-GA) and the Imperialist Competitive Algorithm (ANN-ICA) The results showed that artificial neural network feed forward back propagation (BP-FF) with learning functions Levenberg-Marquardt (LM), Bayesian (BR) and Resilient Backpropagation (RP) has an accuracy of 86, 96 and 82 percent respectively in assigning the features of each vessels to them. Also, ANN-GA and ANN-ICA networks showed classification accuracy equal to 94% and 77%, respectively. Finally, it can be concluded that the Artificial Neural Network with the Bayesian learning function has the ability to acceptably classify the sounds emitted the vessels and can be used in marine and military applications.

Underwater noise radiated by ships is one of the main sources of marine ambient noise and acoustic pollution for marine habitats. Thus, it is essential to determine and assess this type of noise. This study measured the source level directivity of a passenger ship at the frequency band ⅓ octave centered around 63 and 125 in a shallow water environment in the Persian Gulf. To receive the underwater noise of the target ship, a bed-mounted hydrophone array was used. The obtained data were compared with the data from a single B&K-8106 hydrophone located in the middle of the array. The results   suggested that if accurate sound propagation modeling and AIS data are available, it is possible to calculate the source level of ships in shallow water using the bed-mounted hydrophone array. It was   also observed that there is significant variability in the directivity curves at two frequencies of 63 and 125 Hz so that the source level in the angular range of 90° to 165° and 195° to 270° has higher values compared to other angle ranges. That is, there is more noise at the ship stern than at its front.

This paper addresses the problem of direction of arrival (DOA) estimation for nonstationary signals using time-frequency distributions (TFDs). The averaging method is used for selecting the time-frequency points. For this purpose, a strength indicator is used to remove the points with more noise. The method of obtaining the strength indicator is described and numerical simulations are used to show its effect. In addition, the DOA estimation method using time-frequency distributions when the number of sources is more than sensors is described and simulated. In addition to the narrowband model, the broadband model is also used for the output signal of the sensor array, which increases the accuracy of the performance evaluation of DOA estimation methods. The effect of time-frequency kernels, number of snapshots, number of sensors, signal-to-noise ratio (SNR), bandwidth and signal type are investigated in simulations. The effect of window length and window type in the Wigner-Ville kernel is also examined. The computational complexities of both conventional and time-frequency DOA estimation methods are also compared. The results show that as the strength indicator increases, the estimation error decreases, but with a large increase in the strength indicator, the number of averaging points also decreases, which increases the error. Simulation results show that for low SNRs, the TFD based DOA estimation methods perform better, while for high SNR values, both conventional and TFD based approaches have similar performances.

In recent years, the delta wing configuration has been widely used in subsonic drones in addition to supersonic aircrafts such as fighters. On the other hand, due to the increasing development of acoustic detection systems, it is necessary to investigate the noise of drones. In this article, the behavior of surface pressure fluctuations as a source of noise generation on a delta wing with a 70-degree sweep angle has been investigated experimentally. The obtained results show that at angles of attack above 5 degrees, there was a significant increase in the surface power spectral density values, which could be caused by the formation of leading-edge vertices at the end of the wing. Also, by moving in the direction of the flow, while transferring energy from high frequencies to lower frequencies, the peak of the surface pressure spectrum occurs at lower frequencies. Finally, the obtained results show that at an angle of attack of 30 degrees, there was a significant increase in the surface power spectral density values in the position close to the trailing edge of the wing, which indicates the occurrence of the vortex bursting phenomenon at that position.