This work proposes a recognition method to improve the visual detection rate of longitudinal conveyor belt tears related to fog pollution and uneven illumination. It integrates the Haar feature with a modified dark channel defogging algorithm. Firstly, the dark channel algorithm was used to eliminate fog interference with belt images, and the processing time was reduced from 14.5 ms to 5 ms by calculating a single necessary image channel. The Haar feature was used to replace the traditional geometric features due to its high-efficiency feature expression and illumination robustness; this provided a strong feature basis for the learning and training of the classifiers. Next, the classifiers were trained and strengthened by the AdaBoost algorithm, and the strengthened classifiers were cascaded by the Cascade algorithm to improve the recognition ability. Finally, experiments were carried out under mixed fog pollution and uneven illumination. The results show that the average accuracy, recall, precision and false positive rate of the proposed method are approximately 98.4%, 96.9%, 99.9% and 0.1%, respectively, while the recognition period was only 52.3 ms. This shows strong adaptability and robustness to a harsh environment and demonstrates that the proposed method has important application value.

Efficient broadband absorption of low-frequency sound via ultra-thin structure remains challenging due to the narrow-band property generated by the dispersive nature of resonance. In this study, we investigate the absorption mechanism of a component composed of a perforated panel and space-coiling channels through the coupling effect, acoustic impedance matching, and complex frequency analysis. In addition, the influence of geometrical parameters, resonance frequency intervals, and number of components in the coupled system on the band is investigated. Accordingly, the strategy for developing absorbers is to design individual components in the under-damped state by adjusting the geometrical parameters, then put together multiple components with different channel lengths in parallel. On the basis of this strategy, a low-frequency and broadband absorber is theoretically proposed and experimentally demonstrated, which can achieve broadband absorption from 250 Hz to 450 Hz. The design strategy has potential applications in low-frequency noise control engineering, such as plants, automotive and aerospace industries.

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.

Efficient attenuation of low-frequency sound (<1000 Hz) while maintaining unrestricted airflow remains a long-standing pursuit in noise control engineering. Here, a ventilation barrier is integrated with a Helmholtz resonator and an open channel. A density-based topology optimization approach is employed to distribute material within the resonant cavity, effectively reducing noise at target frequencies. For frequencies of 550 Hz, 500 Hz, 450 Hz, 400 Hz, and 350 Hz, the optimized designs achieve transmission losses exceeding 25 dB. Multiple optimized solutions for the same frequencies show minimal variation in sound attenuation performance. Sound pressure and particle velocity distributions are analyzed to reveal the sound insulation mechanisms of the optimized units. Acoustic experiments validate the optimized designs, with results in agreement with numerical calculations. Furthermore, a 48.1% open channel area is maintained, ensuring efficient airflow. Ventilation tests confirm effective air circulation, demonstrating advantages over conventional sound barriers and expanding potential applications in diverse ventilation scenarios.

Smart meter rotation is one of the technical measures to ensure the accuracy of electricity meters. However, the formulation of smart meter rotation plan only focuses on the rotation cycle of smart meters, which is easy to cause great pressure on the rotation workers at the end of the year, and it is difficult to arrange the rotation work for the power company. Therefore, an optimization strategy for the smart meter rotation plan considering time and workload constraints is proposed. The minimum total number of on-site operations in the distribution systems is taken as the optimization goal, and constraints such as monthly workload, holidays, inventory, and rotation cycle are considered. Then, the Cplex solver is used to solve the multivariate linear integer optimization model for smart meter rotation schedule. Finally, case studies on the smart meter rotation plan of Zhejiang electric power corporation in China are performed for verifying the effectiveness of the proposed smart meter rotation strategy. The simulation results show that the workload at the end of the year and the total number of on-site operations in the distribution systems can be greatly reduced by the proposed strategy.