Search

Sparse arrays play an important role in the widely used acoustic and electromagnetic applications such as sonar, radar, and satellite. In this paper, a recently proposed swarm intelligence algorith...

The relatively high probability of elements failure is a concerning problem for large scale phased antenna arrays, which leads to degrade the antenna’s performance significantly. The array performa...

Passive recognition of underwater acoustic targets is a hot research issue in acoustic signal processing. The long-term interference of irregular noise in the marine environment caused the relevance of the passive recognition method of underwater targets based on the traditional technical framework to gradually decrease. Due to the interference of irregular noise in the ocean, the passive recognition method used for underwater targets based on the traditional technical framework is gradually becoming less relevant. The feature extraction method that combines deep learning and time–frequency spectrogram can better describe the differences of different targets. In this paper, the proposed model contains three steps to deal with the recognition of underwater targets: feature extraction, data augmentation and deep neural network. For the feature extraction, we use a Mel-spectrogram, as well as the delta and delta-delta features in order to construct 3-D features. In the data augmentation part, we expand the dataset with SpecAugment in the time domain and frequency domain. In deep neural network prediction part, we use the convolutional recurrent neural network (CRNN) for acoustic target recognition. Through a comparison with the ablation test, it is clear that the pipeline in our method is effective in acquiring the recognition result. After evaluating our system through the carrying out of three tasks on the ShipsEar dataset, and the recognition accuracy are 94.6%, 87.5% and 72.6% in task 1, task 2 and task 3 respectively.

In order to analyze the hydrodynamic performance of the ducted propeller with high precision, this paper proposes a new method which combines Multi-Block Hybrid Mesh and Reynolds Stress Model (MBHM & RSM). The calculation errors of MBHM & RSM and standard two-equation model (standard k-e model) on the ducted propeller JD7704 +Ka4-55 are compared. The maximum error of the total thrust coefficient KT, the duct thrust coefficient KTN, the torque coefficient KQ and the open-water efficiency η0 of MBHM & RSM are 2.98%, 4.01%, 1.46%, and 0.89%, respectively, which are lower than those of standard k-e model. Indeed, the pressure distribution on the propeller surfaces, the pressure and the velocity vector distribution of the flow field are also analyzed, which are consistent with the theory. It is demonstrated that MBHM & RSM on the thruster dynamics analysis are feasible. This paper provides reference in the thruster designing of underwater robot.

Synthesizing antenna arrays with a minimum number of active elements is a problem of high importance in ultrasonic applications. This paper proposes a modified teaching–learning-based optimization (MTLBO) algorithm for thinning and weighting planar arrays to synthesize the desired antenna array factor. Not only the number of active elements and their corresponding excitation weights are optimized, but the peaks of side lobe level, main-lobe width, and current taper ratio are also minimized as objective functions in the multi-objective formulation. The optimization method for designing thinned planar arrays proposed in this work is easy to be implemented and requires few controlling parameters. The simulation cases demonstrate effectiveness of the proposed method, and it is proved that the MTLBO outperforms simulated annealing method and hybrid genetic algorithm through comparisons.

Teaching-learning-based optimization (TLBO) algorithm is a new kind of stochastic metaheuristic algorithm which has been proven effective and powerful in many engineering optimization problems. This paper describes the application of a modified version of TLBO algorithm, MTLBO, for synthesis of thinned concentric circular antenna arrays (CCAAs). The MTLBO is adjusted for CCAA design according to the geometry arrangement of antenna elements. CCAAs with uniform interelement spacing fixed at half wavelength have been considered for thinning using MTLBO algorithm. For practical purpose, this paper demonstrated SLL reduction of thinned CCAAs in the whole regular and extended space other than the phi = 0° plane alone. The uniformly and nonuniformly excited CCAAs have been discussed, respectively, during the simulation process. The proposed MTLBO is very easy to be implemented and requires fewer algorithm specified parameters, which is suitable for concentric circular antenna array synthesis. Numerical results clearly show the superiority of MTLBO algorithm in finding optimum solutions compared to particle swarm optimization algorithm and firefly algorithm.

This paper proposes a modified teaching-learning-based optimization (TLBO) algorithm for synthesis of thinned square array with low side lobe level (SLL). A local search method based on status switch of elements is imposed to speed up the convergence of the TLBO algorithm and enhance the global searching ability of TLBO. Numerical results of thinned square array synthesis show that the performance of the proposed modified TLBO algorithm outperforms other methods through comparison.