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Abstract A method for Underdetermined Blind Source Separation is proposed using third‐order cumulants and tensor compression. To effectively suppress symmetrical distributed noise, the third‐order cumulant is considered. Additionally, the complexity of high‐dimensional tensors can be reduced through high order singular value decomposition (HOSVD) for compression purposes. The method begins by calculating the third‐order cumulant tensor for whitening signals at different time delays, and then stacks several cumulants into a fourth‐order tensor. The HOSVD decomposition is applied to the fourth‐order tensor, compressing the high‐dimensional tensor into a low‐dimensional core tensor. Next, the core tensor is further decomposed using the canonical polyadic decomposition, and the resulting factor matrices are fused to obtain an estimation of the mixed matrix. Finally, leveraging the signal independence, a matrix diagonalisation method is employed to recover the source signals. Theoretical analysis and simulation results demonstrate that the proposed method effectively suppresses the influence of Gaussian noise, reduces computational complexity, and saves computational time. Moreover, compared with five representative approaches, the proposed method achieves superior separation results. Specifically, for the 3 × 4 mixed model with a signal‐to‐noise ratio of 20 dB, the average relative error of speech signal and radio signal are −11.02 and −6.8 dB respectively.

To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance.

Abstract Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single‐Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial‐temporal fusion deep learning model. This is a deep neural network model, which realizes spatial‐temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short‐Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal‐Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal‐Noise Ratio is 0–20 dB and Jamming‐Signal Ratio is 15 dB, the SDR is 5–30 dB higher than the traditional methods and the single network structure deep learning methods.

A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization and histogram stretching are carried out. To alleviate the effect of color shift in an underwater image, the scalar values of R, G, B channels are renewed so that the distributions of the three channels in histogram are similar. Instead of refining the transmittance in dark channel prior based restoration, an optimized contrast algorithm is employed by which the optimal transmittance is determined. To further improve the brightness and contrast of underwater images, a histogram stretching algorithm based on the red channel is given. To verify the effectiveness of the proposed fusion algorithm, experimental underwater images are treated. Results show that the quality of underwater images is improved significantly, both in term of subjective visual effect and objective evaluation. The proposed underwater image processing strategy is also compared with some popular techniques. Comparison results indicate the advantage of the proposed strategy over others.

In the presence of the main lobe jamming, the performance of the modern radar system would degrade significantly. In this study, a blind source separation (BSS) method based on an Adaptive Opposite Fireworks Algorithm (AOFWA) is proposed to address the problem of ineffective target echo detection when the radar is subjected to main lobe jamming. First, a signal model for BSS applied in the anti-main lobe jamming is provided, and the number of source signals is estimated. Then, an adaptive opposite learning operator is designed to improve the optimization capability and convergence speed of the basic Fireworks Algorithm (FWA). Finally, pulse compression is performed for separated signals to achieve peak detection. The simulation result shows that the proposed algorithm can effectively extract target echo under suppression jamming and deception jamming, and the separation precision is relatively high, and its convergence speed is superior.

Aimed at the bottleneck of Firework Algorithm's performance and the problem that it is easy to fall into the local extremum, this paper introduces a chaotic map approach and analyzes its statistical characteristics. New hybrid chaotic systems and a chaotic perturbation operator are designed. Based on different chaotic map approaches, a new Chaotic Firework Algorithm (CFWA) is proposed and simulated by typical test functions. The results show that performance of the Chaotic Firework Algorithm is generally better than the original algorithms, and the chaotic hybrid approaches are superior to single chaotic approaches. In order to further verify the application of the proposed algorithm in practical engineering problems, CFWA is applied to Blind Source Separation (BSS) of radar signals. As a result, it does well in sorting observed signals, and has faster convergence and better sorting performance than the traditional algorithms. In this way, the CFWA extends its engineering application.

A model of a submerged angular cavitation nozzle is established, which consists of a contraction part, parallel middle part, and expansion part. Based on the CFD technique, a numerical simulation of the flow field of the submerged cavitation nozzle is carried out, in which a multiphase mixture model, cavitation model, and renormalization group (RNG) k-ε turbulence model are applied. Considering the influence of mixture density on cavitation, the effects of the inlet contraction part, parallel middle part, and outlet expansion part on the velocity and vapor volume fraction are studied. The numerical simulation results show that the mixture density is essential in the cavitation jet. When the nozzle diameter d is fixed, the designed angular cavitation nozzle with contraction angle α = 13.5°, parallel middle part length Ld = 3d, expansion part length Le = 4d, and expansion angle β = 60° can effectively bring out cavitation. A cavitation cloud is produced near the rigid wall of the outlet expansion section and diffuses in a vortex ring shape. Optimizing the nozzle structure can improve the cavitation effect of the nozzle. The feasibility of this model is verified by relevant experimental data.

To obtain a stabilizer for an underactuated surface vessel with disturbances, an L2-gain design is proposed. Surge, sway, and yaw motions are considered in the dynamics of a surface ship. To ob-tain a robust adaptive controller, a diffeomorphism transformation and the Lyapunov function are employed in controller design. Two auxiliary controllers are introduced for an equivalent sys-tem after the diffeomorphism transformation. Different from the commonly used disturbance ob-server-based approach, the L2-gain design is used to suppress random uncertain disturbances in ship dynamics. To evaluate the controller performance in suppressing disturbances, two error sig-nals are defined in which the variables to be stabilized are incorporated. Both time-invariant dis-continuous and continuous feedback laws are proposed to obtain the control system. Stability analysis and simulation results demonstrate the validity of the controllers proposed. A comparison with a sliding mode controller is performed, and the results prove the advantage of the proposed controller in terms of faster convergence rate and chattering avoidance.

The parameters of a well predicted model can be used as health characteristics for Lithium-ion battery. This article reports a parallelized parameter identification of the thermal-electrochemical model, which significantly reduces the time consumption of parameter identification. Since the P2D model has the most predictability, it is chosen for further research and expanded to the thermal-electrochemical model by coupling thermal effect and temperature-dependent parameters. Then Genetic Algorithm is used for parameter identification, but it takes too much time because of the long time simulation of model. For this reason, a computer cluster is built by surplus computing resource in our laboratory based on Parallel Computing Toolbox and Distributed Computing Server in MATLAB. The performance of two parallelized methods, namely Single Program Multiple Data (SPMD) and parallel FOR loop (PARFOR), is investigated and then the parallelized GA identification is proposed. With this method, model simulations running parallelly and the parameter identification could be speeded up more than a dozen times, and the identification result is batter than that from serial GA. This conclusion is validated by model parameter identification of a real LiFePO 4 battery.

Combining particle filter (PF) with sample entropy feature of discharge voltage, a method of remaining capacity estimation for lithium-ion battery is proposed. The sample entropy calculated from discharge voltage curve can serve as an indicator for assessing the condition of battery. Under a certain working condition, a functional relationship between sample entropy and discharge capacity is created and estimations computed from the function are taken as observations to propagate particles in PF. The results indicate that the algorithm enhances the accuracy. Due to the establishment of functions at different discharge rates and temperature modification, prognostic accuracy of discharge capacity has been improved under multi-operating working conditions.

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.