In this paper, the mechanism of enhanced membrane separation by ultrasound is analyzed. The main factors affecting the separation of enhanced membrane by ultrasonic technology is studied, the application and research progress of ultrasonic technology in membrane separation are studied. The conclusion are (a) cavitation caused by ultrasonic radiation is the main factor that increases the permeation flux of dextran solution (b) higher intensity of sound is helpful to the cleaning of the film; (c) ultrasound enhancement is related to temperature and operating pressure; (d) the film assembly shell material also affect the ultrasonic effect. In addition, the influence of ultrasonic wave on membrane structure is also discussed. Result indicates that the membrane separation process under the action of ultrasound does improve its permeability, but sometimes ultrasound can also cause damage to the membrane. Application of ultrasonic techniques for enhancing the separation process of membrane is also reviewed.

A new method for locating hazardous gas source based on unmanned vehicles is presented in this paper. Based on the gas sensors and unmanned vehicles, the research on the gas source location algorithm, using the gas concentration of several detection sites as heuristic information, is carried out. When the available information is less, such that the gas diffusion model is unknown, the algorithm can locate the gas leakage source quickly. The proposed algorithm combines particle swarm optimization (PSO) and Nelder–Mead simplex method. Compared with the standard PSO, the proposed algorithm has fewer iterations and faster convergence speed. Finally, the feasibility of the algorithm is verified by digital simulation experiments.

A new method is proposed for generating forest fire-fighting tasks using an improved Sliding Window-based Weighted Density-Based Spatial Clustering of Applications with Noise (SW-DBSCAN). To reduce the neighborhood search range of core objects and improve the clustering accuracy of key protected object, fire head, fire tail, and fire flank on the fire line, a sliding window is introduced, and a weighted similarity distance measurement method is designed in the DBSCAN framework. At the same time, evaluation indicators are proposed to evaluate the effectiveness of this method. Finally, several numerical simulations show that this method is feasible and flexible for generating forest firefighting tasks.

Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Traditional fire detection methods mainly include three categories, which are manual inspection, sensor detection, and visual recognition. However, manual inspection is restricted by labor costs and time efficiency, making it difficult to achieve large-scale, high-frequency and real-time fire monitoring. Sensor detection is easily interfered by environmental factors such as temperature, humidity, and dust, leading to frequent false alarms and missed alarms. Visual recognition technology has shortcomings in aspects such as detailed feature perception, dynamic scene modeling, and reasoning robustness in complex environments, making it difficult to meet the requirements of high-precision detection. To address these issues, this study innovatively proposes a lightweight fire and smoke detection model based on semantic enhancement and frequency domain perception modeling, which is named the SemaFire you only look once (SemaFire-YOLO) model. The model constructs a large language and vision assistant (LLaVA) semantic guidance module, which uses a large language model to understand and guide the semantic features of images, thereby enhancing the saliency representation intensity of small and weak target regions. Then, a Haar wavelet-based downsampling module is adopted, which compresses spatial information while preserving high-frequency features such as flame edges and smoke textures, improving the accuracy of target recognition. Next, the convolution modulation mechanism is introduced to replace the traditional attention mechanism, enhancing the overall modeling efficiency and reducing computational overhead. Finally, a Dynamic Tanh normalization module is adopted to replace the batch normalization module in the traditional YOLO algorithm, strengthening the model’s representation stability and reasoning robustness under unstable input distributions. Experimental results show that the SemaFire-YOLO model achieves a mean average precision (mAP@0.5) of 64.30% on the fire image dataset, which is 0.8, 2.0, 0.6, and 3.8 percentage points higher than that of mainstream models such as YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, respectively. It exhibits better boundary detection capability and practical deployment potential. Through visual analysis, the results indicate that the improved SemaFire-YOLO model achieves more accurate detection and higher confidence in actual complex scenarios, further verifying the model’s robustness and accuracy in complex scenarios such as low contrast and dynamic fire conditions.

Cooperative maritime salvage using multiple unmanned helicopters involves coupled cable forces and changes in seabed suction, hydrodynamic drag, and buoyancy. This paper studies helicopter position tracking during seabed release, underwater lifting, partial emergence, and aerial transport. A simplified coupled dynamics model is combined with finite-time prescribed-performance control (FPPC) and an unknown system dynamics estimator (USDE). The performance boundary reaches a prescribed final value at a user-selected time, while the estimator reconstructs lumped cable-induced disturbances for compensation. The control structure includes position, velocity, attitude-angle, and angular-rate loops. Numerical comparisons with DSC-USDE are conducted for cuboid and cylindrical payloads. The archived controller-error records indicate smaller tracking errors and improved performance-boundary retention in the considered nominal scenarios. These results provide a numerical assessment of the proposed controller within the assumed simulation model; they do not constitute experimental validation of the complete physical salvage system.