Enhancing infrared images is essential for detecting wind turbine blades using infrared technology. This paper introduces an Infrared Image Enhancement Method based on Adaptive Iterative Cutoff Threshold Difference Multi-Scale Top-Hat Transformation (AICT-DMTH) to address the challenge of low image clarity in infrared detection. The method involves performing a black-white difference top-hat transformation by utilizing structural elements of varying scales for dilation and erosion. Additionally, an iterative threshold method is applied to extract more detailed image features, followed by setting a cutoff constant to determine the final scale of the structural element. The effectiveness of the proposed method is evaluated both qualitatively and quantitatively, with infrared images from laboratory and wind farm settings enhanced and compared against existing methods. The experimental results indicate that the proposed method significantly improves the clarity of infrared images, demonstrating robustness in enhancing images from various environments.

As a swarm intelligence algorithm, Dung beetle optimizer (DBO) was inspired by the behavior pattern of dung beetles for survival. This paper presents a dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion (MMDBO), which addresses the slow convergence and weak global search of the original DBO. MMDBO improves performance by incorporating a cosine similarity strategy for position updates, enhancing convergence speed and diversity. The MFDB strategy is then applied to balance global exploration with local exploitation. Additionally, the Hypotrochoid and Levy flight strategies are used to enhance search ability and solution quality, while a non-uniform mutation strategy is introduced to avoid local optima. To comprehensively evaluate the optimizer performance of MMDBO, experiments were conducted on the IEEE CEC2017 and CEC2022 benchmark sets, comparing it with 13 other population-based optimizer algorithms. The experimental results show that MMDBO achieves Friedman mean rankings of 1.21, 1.52, and 1.52 for the 30-dimensional, 50-dimensional, and 100-dimensional problems on the CEC2017 benchmark set, respectively, and a ranking of 1.5 for the 20-dimensional problem on the CEC2022 benchmark set. These results indicate that MMDBO consistently outperforms most algorithms and provides accurate and reliable optimizer solutions. Additionally, MMDBO’s practicality is demonstrated through five real-world constrained engineering design challenges, including a wind farm layout optimizer problem and a magnesium alloy constitutive model parameter identification problem, further validating its broad applicability in real engineering problems. The results of the study indicate that MMDBO possesses excellent optimizer capacity and broad application potential.

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.