The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.

This study proposes an innovative image encryption algorithm based on the DNAS_box and hyperchaos. The algorithm dynamically constructs a DNAS_box using 2-bit deoxyribonucleic acid (DNA) coding and 4-bit DNA-like coding, enabling seamless conversion between these two coding schemes. The implementation process comprises four key phases. First, a hyperchaotic system generates chaotic sequences while dynamically encoding the plaintext image according to DNA coding rules. Next, the 2-bit DNA keys undergo encoding before performing XOR operations with the encoded plaintext image. Subsequently, under chaotic sequence control, the DNAS_box transforms 2-bit DNA symbols into 4-bit DNA-like symbols. Finally, XOR operations are executed between the 4-bit DNA-like symbols and corresponding DNA-like keys, producing a 4-bit DNA-like symbol sequence. This sequence is dynamically decoded under chaotic sequence guidance to generate the ciphertext image. The algorithm’s effectiveness is validated through MATLAB-based numerical simulations, with experimental results confirming its robust security performance. Notably, the algorithm demonstrates a key space of approximately 10¹⁴⁵, an NPCR exceeding 99.5%, and ciphertext entropy surpassing 7.997.

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind–solar generation with hybrid electric–hydrogen energy storage systems to simultaneously enhance economic efficiency and system stability. The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale Optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. By integrating electric–hydrogen hybrid storage technology, the inner layer effectively mitigates power fluctuations. Furthermore, this study designs a modal decomposition-based charging and discharging scheduling strategy to ensures the system’s continuous and stable operation. Simulations performed on MATLAB 2018b and CPLEX 12.8 platforms indicate that the proposed dual-layer model decreases annual total expenses by 27.5% compared to a single-layer model while keeping grid-connected power variations within 10% of the installed capacity. This research provides innovative perspectives on microgrid optimization design and offers substantial technical support for ensuring stability and economic efficiency in intricate operational settings.

In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study proposes a self-adaptive K-SVD (SAK-SVD) denoising algorithm based on adaptive window parameter optimization, establishing a closed-loop iterative feedback mechanism through dual iterations between dictionary learning and parameter adjustment. This approach achieves a synergistic enhancement of noise suppression and signal fidelity. First, a dictionary learning framework based on K-SVD is constructed for initial denoising, and the peak feature region is extracted by differentiating the denoised signals. By constructing statistics on the number of sign changes, an adaptive adjustment model for the window size is established. This model dynamically tunes the window parameters in dictionary learning for iterative denoising, establishing a closed-loop architecture that integrates denoising evaluation with parameter optimization. The performance of SAK-SVD is evaluated through three experimental scenarios, demonstrating that SAK-SVD overcomes the rigid parameter limitations of traditional K-SVD in FBG spectral processing, enhances denoising performance, and thereby improves wavelength demodulation accuracy. For denoising undistorted waveforms, the optimal mean absolute error (MAE) decreases to 0.300 pm, representing a 25% reduction compared to the next-best method. For distorted waveforms, the optimal MAE drops to 3.9 pm, achieving a 63.38% reduction compared to the next-best method. This study provides both theoretical and technical support for high-precision fiber-optic sensing under complex working conditions. Crucially, the SAK-SVD framework establishes a universal, adaptive denoising paradigm for fiber Bragg grating (FBG) sensing. This paradigm has direct applicability to Raman spectroscopy, industrial ultrasound-based non-destructive testing, and biomedical signal enhancement (e.g., ECG artefact removal), thereby advancing high-precision measurement capabilities across photonics and engineering domains.