In order to obtain the location of erosion gullies in Bayan County, understand the spatial relations between multi-features and erosion gullies, assessing the occurrence risk, and provide information and method reference to erosion gully management and precaution, high-resolution satellite imagery, digital elevation model, soil, and precipitation data were used to acquire erosion gully locations and topographical, soil, hydrological, meteorological features. Subsequently, the spatial relations between erosion gullies and multi-feature were analyzed, and erosion gully occurrence risk was assessed using the random forest method. As results, towns like Waxing, Dexiang, and Xinglong could be paid more attention on the erosion gully management. Elevation relief, slope, slope factor, slope length factor, water flow density, and distance to water flow tend to show more spatial relations with erosion gullies, and erosion gully would participate in the redistribution of soil nutrition. On the other hand, for natural or data spatial resolution reasons, slope aspect, terrain curvature, catchment quantity, rainfall erosivity factor, soil bulk density, and soil erodibility factor show little spatial relations with erosion gullies. Assessment indicates that the erosion gully risks are mainly in arable lands with slopes between 1.5°~6°. Results indicates that topographical and hydrological features are closely related to erosion gullies, and diagonal ridge is an economical and effective measure.

In dynamic wireless environments, the distortion of transmission waveform is inevitably present, deteriorating the accuracy of identifying high altitude electromagnetic pulse (HEMP) parameters. To address this issue, an extreme learning machine parameter identification network (ELM-PInet)-based parameter identification method was investigated, which leverages the characteristics of HEMP waveform and considers the impact of wireless channels, thereby improving the accuracy of HEMP parameter identification. To demonstrate the nonlinear effects of wireless channels, the transmission model of HEMP waveform was first constructed based on wireless transmission theory. Subsequently, an ELM-PInet was developed to suppress waveform distortion and improve the identification accuracy of HEMP parameters. Finally, the proposed method was validated through field irradiation test on the experimental platform. Simulation results demonstrate that compared to classical HEMP parameter identification methods, the identification accuracy of HEMP parameters is enhanced by the proposed method. Furthermore, the ELM-PInet-based parameter identification method exhibits its robustness against the impacts of different parameters. Additionally, the effectiveness of the proposed method is further validated through field irradiation experiments.

Concrete arc beams in the support mold is often difficult to ensure the molding accuracy, the production is more difficult to high cost, and 3D printing technology has a construction speed, design freedom and high characteristics, so in order to solve the problems such as the complexity of concrete arc beam support, the effectiveness of 3D printing arc shell-cast-in-place beam construction was studied. According to the existing 3D printing concrete ratio and process parameters, three 3D printing curved beam mold shells were designed and printed, and the printing and molding accuracy was measured. The mold shells were equipped with reinforcing cages and cast-in-place concrete materials, and 3D printing concrete curved mold shells-cast-in-place beams were produced. The beam specimens were subjected to vertical loading tests to validate the effectiveness of the construction method. The results show that the 3D printed curved mold shell is basically the same size as the 3D model, with a maximum error of 4% in the middle, and the overall printing and molding quality is good. Under vertical loading, the damage patterns of the three 3D printed curved mold shell-cast-in-place beam specimens are similar. The cracking load and ultimate capacity of the beam specimen with reinforcement between the curved mold shell and the cast-in-place beam have been significantly improved, with an increase in the ultimate load of about 25%.

For full-bridge LLC resonant converter, intermittent control strategy is an effective means to improve its light load efficiency. An improved intermittent control method was proposed to solve the problems of limited efficiency improvement and large output voltage ripple in light load state of converter by traditional intermittent control. This control method fixes the intermittent conduction time and makes the converter work at the resonant frequency during the intermittent conduction time, which further improves the light load efficiency of the converter and reduces the output voltage ripple. In order to verify the feasibility of the proposed method, a simulation model was built and the simulation waveforms of the traditional intermittent control strategy and the improved intermittent control strategy were compared and analyzed, and an experimental prototype with rated power of 100 W was made. The simulation and experimental results show that the output voltage ripple of the full-bridge LLC resonant converter with the improved intermittent control strategy can meet the engineering requirements, and compared with the traditional intermittent control strategy, the efficiency under light load is improved by up to 4.1%.

Proppant performance is very important to the hydraulic fracturing design of unconventional oil and gas reservoirs. Few scholars have studied the micro performance parameters of proppant in terms of particle size and shape. The effect of particle size and shape on proppant breakage rate and fracture conductivity in shale gas reservoir was quantitatively characterized through laboratory experiments. The results show that when the closing pressure is lower than 28 MPa, the same type of proppant with uniform particle size and high spherical degree is compared with the proppant with poor sorting, the crushing rate is reduced by 15% and the fracture conductivity is increased by 10%. When the closing pressure exceeds the compressive strength of the proppant, the well-separated proppant can maintain the fracture conductivity better as the flow channel is further blocked by the debris generated by the broken proppant. The experimental results provide a reference for in-situ fracturing design of shale formation, improving the quality control level of downhole materials and selecting proppant.

Composite material is widely used in aerospace field. It is important to research the damage process of composite and its failure mode. Composite damage is a complex progressive process. In order to predict the strength and damage propagation of reinforced composite wall panels under compression conditions, taking the cap shaped single rib panel under compression load as an example, one instantaneous stiffness degradation model and three continuous stiffness degradation models were used for analysis and comparison. By reducing the stiffness of material points in the element, the damage evolution process of composite materials was simulated, and the experimental results were compared with the analysis results. The comparison results show that all four damage degradation models can accurately predict the bearing capacity and damage range of reinforced wall panels under compression conditions. Compared with other models, the constant type model in the continuous damage degradation model has the highest accuracy. The research results provide theoretical guidance for the study of mechanical properties of composite reinforced wall panels.

The issues of idler blocking during belt conveyor operation were addressed, which leads to excessive friction and abnormal temperature rise between idlers and conveyor belts. A friction surface temperature rise model for faulty idlers and conveyor belts was established based on microscopic friction theory, considering the phenomenon of hysteresis-induced heat generation and utilizing the virtual work approach. The finite element method was employed to conduct a thermo-mechanical coupling simulation on the friction model to analyze the effects of belt speed and load on temperature rise. An experimental platform was constructed to investigate the heat generation from friction between faulty idlers and conveyor belts, where an infrared thermal imager was utilized to monitor the temperature rise under varying parameters. The results indicate that the friction-induced heat generation between faulty idlers and conveyor belts positively correlates with both belt speed and load. An increase in either factor results in heightened heat generation, with the heat being primarily concentrated on the surface of the faulty idlers. The maximum deviation between experimental values and theoretical calculations is 8.7%, confirming the reliability of the theoretical model. Corresponding measures are proposed based on these findings.

Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the “-v” method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm’s optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R²) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.

Aiming at the problems such as small and medium-sized obstacles on the road are prone to miss detection, small target obstacles are difficult to detect, and the number of model parameters is large in smart driving scenarios, the obstacle target detection algorithm with improved YOLOv8n was proposed. Distribution shifting convolution (DSConv) was used in the backbone network to replace floating point operation with integer operation, reducing the amount of redundant computation, and maintaining the accuracy by imitating the original convolution layer by quantization and distribution shifting. By adding small target detection layer, the feature information of small target can be captured better and the scale characteristics of small target can be adapted. Combined with SimAM parameterless attention mechanism, SPPF-SimAM module was introduced to improve the quality and diversity of feature representation, and the detection accuracy was improved without increasing the number of parameters. By combining ghost-shuffle convolution (GSConv) and VoV-GSCSP modules, the neck feature fusion network was lightweight, reducing the number of parameters and calculation of the model. The experimental results show that the accuracy, recall, and mean average precision of the improved model are improved by 1.6%, 8.0%, and 6.2%, respectively. The number of parameters is reduced by 6.7% compared with the original model, and the proposed algorithm effectively improves the detection accuracy of small and medium-sized obstacles in smart driving scenarios, and achieves a better balance between the detection performance and the model lightweighting.

In order to solve the problem of poor image denoising performance caused by the simple encoder-decoder structure of the convolutional neural network image denoising model, a residual dense image denoising network (RDIDNet) based on the residual dense network and attention mechanism was proposed. Firstly, the global residual block was used to enhance the nonlinear mapping ability of the network model. Secondly, the double-element convolutional attention module was introduced to realize the adaptive feature fusion in the decoding process of RDIDNet model. Finally, the RDIDNet denoising model was compared with 14 representative denoising methods, and ablation experiments were conducted to verify the effectiveness of using RDU Sub Network, DE-CAM, and PSNRLoss for network optimization on the benchmark model. The experimental results show that in the Set12 dataset and BSD68 dataset, RDIDNet improves the peak signal to noise ratio (PSNR) and structural similarity (SSIM) metrics by an average of 1.03 dB and 0.027 5, respectively, compared to the traditional classical method BM3D. Compared to SwinIR based on Vision Transformers architecture, the average improvement is 0.03 dB and 0.001 4, respectively. Compared to the latest CNN based denoising method NHNet, it has an average improvement of 0.22 dB and 0.008 9. The RDIDNet denoising network focuses more on low-frequency information and has more stable model training. It can effectively eliminate image noise while preserving image details and textures, and has good performance.