In order to study the influence of floating offshore wind power suction anchor size on the horizontal bearing capacity, based on the ABAQUS finite element software, a three-dimensional finite element model of suction anchor foundation was established by using elastic-plastic constitutive model. The results show that the increase of diameter and height can improve the horizontal bearing capacity of suction anchor, and the increase of height is more obvious. With the change of diameter and height, the position of the mooring point also needs to be adjusted accordingly. When the ratio of suction anchor diameter to height D/H>1, the position of the mooring point needs to be increased correspondingly to make it move in translation. The change of diameter will affect the pressure change of the anchor wall, while the height has little effect on the pressure change of anchor wall. The research results are used in global first offshore floating wind power + aquaculture platform “Guoneng Gongxiang Hao” and can provide a reference for relevant project design.

The cathode flow channel of proton exchange membrane fuel cell (PEMFC) serves as the site of oxidant reduction, and the interaction of the flow channel configuration and operating parameters is one of the keys to enhance the performance of the cell. A three-dimensional proton exchange membrane cell model with cathode sidewall shrinkage runner was established to meet the design requirements of PEMFC sidewall shrinkage runner regarding the operating parameters. The changing rules of electrochemical performance, temperature distribution on the membrane surface and water content distribution were investigated under different temperatures, pressures and cathode stoichiometric ratios. It is shown that under the constant operating parameters, the current density curves of the ridge centerline and the flow channel centerline are impulsively fluctuated, and the temperature curves of the membrane surface and the water content curves of the membrane surface are regularly fluctuated. The current density, temperature and water content at the ridge centerline are obviously higher than those at the flow channel centerline. Under the change of operating parameters, when the pressure is increased from 0.1 MPa to 0.3 MPa, the current density is increased from 0.860 A/cm² to 1.500 A/cm², with an increase of 74.4%. When the temperature is increased from 50 ℃ to 80 ℃, the current density is increased from 0.822 A/cm² to 0.856 A/cm², with an increase of 4.1%. And when the cathode stoichiometry ratio is increased from 10 to 90, the current density is increased from 1.502 A/cm² to 1.568 A/cm², with an increase of 4.4%. Furthermore, a PEMFC output performance evaluation method based on the combined assignment method and the improved radar diagram method has been established. The cathode sidewall-retracted proton exchange membrane fuel cell is shown to exhibit excellent output performance under the operating parameters of 0.25~0.3 MPa, 70~80 ℃ and the stoichiometric ratio in the range of 70~90.

As the trend toward more-electric and all-electric aircraft accelerates, multi-electric engines have become a key technology. A coaxial high-torque permanent magnet synchronous motor based on a new type of rotary cylinder disc engine was designed to achieve the integration of the engine and the motor. Firstly, based on the relationship between the engine performance parameters and the drive shaft, the coaxial structure and motor dimensions were determined. Secondly, the inhibitory effect of the number and size of the flat wire winding layers on copper loss was analyzed through the finite element soft analysis. Meanwhile, the motor topology was optimized by using the rotor segmented inclined pole, auxiliary slot and Taguchi algorithm to reduce the cog-slot torque, rated torque ripple, stator iron loss and air-gap magnetic flux density distortion rate of the motor, significantly improving the electromagnetic performance. Finally, the various working conditions of the motor were simulated, the driving and power generation efficiencies were calculated, and it is verified that the motor does not demagnetize under the condition of large current. Results indicate the motor delivers 200 N·m of torque at a rated speed of 6 000 r/min, with a peak torque of 400 N·m and a maximum power output of 250 kW in high-speed generation mode, meeting all design specifications.

The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.

With the rapid development of the power system, the large-scale integration of new energy into the grid and the coordinated optimization of source-grid-load-storage have increased the proportion of power electronic equipment, making the stability of the power grid, especially the assessment of transient stability, particularly important. Aiming at the problem of insufficient consideration of topological structure in traditional methods, a deep learning method based on Transformer-graph attention network(GAT) parallel feature fusion was proposed for the transient stability evaluation of power systems. The busbar voltage amplitude, phase angle and topological adjacentation matrix were taken as input features. Batch data were generated using the Siemens simulation software PSS/E, and features were extracted in parallel through Transformer and GAT. Weighted fusion was carried out using the attention mechanism. The comparison results with other methods show that this method simulates different load conditions and fault conditions in the IEEE 39-node system. The results indicate that the evaluation accuracy and robustness are relatively high, and it can effectively improve the safety and stability of the power system.

The stepped frequency ground penetrating radar has the advantages of high sensitivity, large dynamic range, and high average power. Radio frequency system on chip(RFSOC) based stepped frequency ground penetrating radar transceiver system design was designed and implemented. The system mainly includes modules such as the transmission link, reception link, clock unit, field programmable gate array(FPGA) control system, and data transmission unit. By setting multiple numerically controlled oscillator (NCO) for synchronization between the transmission and reception ends, the carrier was synchronized on the same frequency and phase, ensuring the phase coherence of radar transmission and reception signals. A receiving time window was also designed in the data transmission unit. The experimental test results show that the system can achieve the transmission and reception of step frequency signals with a frequency range of 200 MHz~2 GHz and a step size of 2 MHz, and can effectively detect multiple scene targets such as free space, sand pits, and asphalt pavements.

In order to solve the problem of uneven data distribution or confusion of characteristics in material supply chain under multi-source data, a data balance processing method of material supply chain based on multi-source data was proposed. The data distribution space of material supply chain was established, non-boundary and boundary areas were set, the center of the area was calibrated, the distances between different characteristic data and the center point were calculated respectively, the unbalanced data was searched in advance by using the edge mixed sampling algorithm, and the boundary area to which it belongs was determined according to the data distance characteristics. The unbalanced data was regarded as the working nodes in the supply chain cluster. When the number of working nodes in the cluster changes, the load generated by each cycle execution node was obtained by flow calculation, and the load value was converted into the execution thread score. The unbalanced data was obtained by comparing the scores, the threshold of data memory and CPU resource consumption in the material supply chain was calculated, and the parallel task thread was established. The scheduler was used to transfer the execution thread in the working node to the next node to achieve the purpose of load balancing. The experimental results show that the proposed method has the advantages of short response time, large data throughput, good processing effect and strong stability, and has good practical application value.

In order to achieve accurate segmentation of surgical instruments, a dual-encoding network surgical instrument segmentation method was proposed based on improved Swin Transformer. By taking advantage of different coding advantages of Swin Transformer and convolutional neural network(CNN), the global semantic information and local details of image features can be effectively captured to improve the expression ability of the model. To compensate for the loss of feature details during the downsampling process as much as possible, the multi-resolution feature pyramid pooling(MFPP) block was constructed to obtain more scale context information by combining different dimensional features and enhance the expression of local detail information. Finally, a coordinate attention block was added in the skip connection to fuse target position information with feature information for precise perception of the surgical instrument targets. The experimental results show that the proposed method achieves more accurate segmentation results in both binary and parts segmentation of surgical instruments, further verifying the effectiveness and accuracy of the proposed method.

Urban roads are recognized to facilitate daily human activities while simultaneously being observed to shape behavioral patterns. Currently, most studies simply analyze the relationship between roads and crime distribution as part of the built environment, and few studies have thoroughly explored the impact of different structural attributes of roads on crime. In order to further explore this impact mechanism and provide guidance for the optimization of police resource allocation by front-line police departments. The operational mechanisms were investigated by which geometric and topological road attributes influence theft distribution across varied transportation modalities. Using crime data from YC District, JB City, spatial crime hotspots and high-risk road segments were initially identified through geospatial analysis. Next, the road structure was systematically decomposed into two distinct dimensions: geometric attributes and topological properties. A space syntax segment model was created to quantitatively assess geometric characteristics and traffic modality-specific topological configurations. Finally, Statistical relationships were investigated using zero-inflated negative binomial regression complemented by multiple linear regression modeling. Research has found that theft incidence is demonstrated to be positively associated with segment length, angular curvature metrics, and branch road density within the network. Elevated crime probabilities are observed in extended roadway segments exhibiting complex geometric configurations and regions characterized by dense branch road networks. The closeness and betweenness of the road's topological structure exert varying significant effects on theft crime across different traffic modes. Specifically, in pedestrian traffic modes, a significant negative correlation is observed between road closeness and betweenness and the occurrence of theft crime. In bicycle traffic modes, road closeness and betweenness are found to have a positive effect on theft crime. In electric vehicle and motor vehicle traffic modes, road closeness have a positive effect on theft crime, while in electric vehicle traffic mode, betweenness was found to have a negative effect on theft crime. These findings contribute novel insights to environmental criminology theory while offering empirically grounded recommendations for strategic police deployment and urban security management.

Image segmentation is a fundamental problem in medical image analysis, the typical deep learning based UNet architecture (UNet) and its variants are widely used in retinal vessel segmentation. However, the UNet network extracts feature information from images through local convolution modules, which makes the global information of the images difficult to be correlated and the long-distance dependencies between pixels difficult to be effectively captured. Considering the problems with the UNet network model and the characteristics of retinal vascular images, an attention module was added to the skip connections of UNet to capture long-distance dependencies between blood vessels. In addition, to enhance the segmentation ability of the network, the group normalization(GN) was used instead of the original batch normalization (BN) of the UNet network model, and the corresponding groups were selected for different channels. To update parameters and optimize the network, the final cross entropy loss function was designed using the side output layer and the final output layer. Experiments are implemented on the DRIVE dataset and CHASEDB1 dataset, and the experimental results show that the proposed model has better image segmentation performance.