To solve the problems of discontinuity in path curvature, low parking efficiency and low path tracking accuracy in autonomous parallel parking, the characteristics of the circular arcstraight linecircular arc initial parking path were analyzed, and the path planning method based on  fifthorder polynomial curve was adopted. To achieve the balance between path length and path curvature performance, based on the constraints of maximum path curvature, required parking space and obstacle avoidance, the weighted sum of the maximum curvature and the horizontal coordinate of parking start point was used as objective function. The particle swarm optimization algorithm with nonlinear dynamic adaptive inertia weight was employed to optimize the horizontal coordinate of the parking start point, and after optimization, the path became smooth and continuous in curvature. The path tracking control method based on model predictive control was established, and the genetic algorithm was used to optimize the prediction horizon and control horizon for reducing computational load while ensuring tracking accuracy. The real vehicle validation was conducted based on the autonomous driving developer kit of Baidu Apollo. The results show that the vehicle parking can be completed safely without collisions, which verifies the feasibility of the path planning method. Under the premise of reduced computation load, the average path tracking error is decreased by 4.348% compared to that before optimization, and more high tracking accuracy can be obtained by the proposed path planning method.

To improve the driving safety, the fault diagnosis model integrating local correlation constraints and signed directed graph (SDG) was proposed to address the challenge of fault localization in automatic emergency braking (AEB) systems. Taking the AEB system based on the time-to-collision (TTC) control strategy as research object, the SDG model of  braking system was constructed by combining vehicle dynamics model, brake pressure model and sensor system layout. To verify the proposed model, the cosimulation of Carsim and Simulink was conducted. The results demonstrate that the SDG model based on graph theory offers strong interpretability and effectively reflects fault propagation paths. Through the cosimulation tests of Carsim and Simulink, the fault diagnosis and localization during the active braking process can be efficiently achieved. By the propose method, the abnormal internal connection nodes can be detected within approximately 10 diagnostic function computations with accurate fault localization and propagation judgment.

Based on the regulations of GB 13057—2023 "Strength of Seats and Their Anchorages of Passenger Vehicles", the optimization design was conducted for the anchorages of a certain bus seat. The side length of square tube, the wall thickness of square tube, the thickness of end cap, the thickness of side reinforcement component and the thickness of thinwalled parts used for upper and lower connections were selected as design variables. The adaptive Kriging (AKR) agent model for the seat forward displacement was established. The mass of the vehicle anchorage was calculated by the Catia parametric design method. With seat forward displacement and anchorage mass as optimization objectives, the multiobjective optimization of seat anchorage dimensions was performed by the particle swarm optimization (PSO) algorithm. The optimization results show that the seat forward displacement is reduced to 443.22 mm, which is decreased by 7.85% compared to that before optimization and can significantly enhance the seat rigidity for providing greater safety for occupants. The mass of the vehicle anchorage is reduced by 11.08% for achieving the goal of lightweight design.

To quickly and accurately detect the feature information of track cone buckets in the formula unmanned competition, the detection method fusing LiDAR point cloud data and camera image data was proposed. The region of interest (ROI) was extracted from the point cloud data, and the noise was filtered.The adaptive ground segmentation algorithm was introduced for the current ground undersegmentation and oversegmentation problems, and Euclidean clustering was used to extract the spatial location information of cone buckets. The image dataset was collected and labeled, and the posttraining detection was performed by YOLOv5 to complete the extraction of cone bucket color information. The bucket spatial position information detected by LiDAR and the bucket color information detected by camera were fused.The experiments were designed for validation. The results show that the proposed fusion detection method can solve the problems caused by single sensor detection of cone buckets, and it can quickly and accurately detect the cone buckets in the track, which provides guarantee for planning and decision making.

The intake flow rate and pressure in the air supply system of proton exchange membrane fuel cells(PEMFC) are two coupled control variables, and the coupling affects the stability of the fuel cell system. According to the decoupling control requirements of intake air flow and pressure, the system openloop response experiment was designed, and the system identification method was used to obtain the model parameters of the system transfer function matrix. On this basis, the double closedloop PI, feedforward decoupling and fuzzy decoupling control strategies were designed. The decoupling effects under different control strategies and the antidisturbance abilities of different control strategies to system parameter changes were compared and analyzed. The simulation results show that the coupling effect between flow rate and pressure is significant under dual closedloop PI control. By the feedforward decoupling control, the complete decoupling can be achieved, but the decoupling effect is affected by the parameters of the controlled object model. The fuzzy decoupling control has good decoupling effect and stronger adaptability to parameter changes compared to conventional feedforward decoupling control, which has better application prospect in the multivariable strong coupling system control.

Using salt and tap water as raw materials, the hypochlorite solution was produced by electrolysis for using as substitute disinfectant in public places, and the substitute disinfectant was integrated into local synthesis spray sterilizer. The device was composed of two parts with pneumatic electrostatic atomization unit and disinfectant local synthesis unit. The effective operating distance was 6 m with unit time spray volume of 303 mL/min. In the E. coli test, the ORP value of the test group was high, and the change range was small when the salt water with mass fraction of  2% was reacted for 3 min. Compared to the pure water control group, the sterilization rate of the selected test group was 100.00%, and sterilization effect was significant.The spray performance test was conducted to analyze the spray performance under different conditions by changing the air pressure with or without  charging.The results show that as the air pressure is changed, the amount of spray per unit time and the spraying width and distance are changed accordingly. At the air pressure of 2.5×105 Pa, the spray volume, spray distance and spray range per unit time are 303 mL/min, 6.00 m and 1.25 m, respectively. In the charged state, the spray amplitude, deposition density and deposition amount are larger than those in the same condition. At the air pressure of 2.0×105 Pa, the maximum spray amplitude is 1.21 m, and the spray amplitude is increased by 0.20 m compared with that at noncharged condition. The deposition density of droplets can be increased by charging, and the deposition amount is increased by 0.08 μL/cm2 at the upper position of 3.70 m. At the air pressure of 2.5×105 Pa, the average particle size of droplets is 47.93 μm. The experimental results show that the total number of bacteria is less than or equal to 300 CFU/（25 cm2） when the developed substitute disinfectant local synthesis spray disinfection device is used in laboratories and public places, which meets the national standard.

To investigate the influence of gear pair installation error on transmission error (TE), a pair of cylindrical helical gears carried on the main gearbox of a heavy commercial vehicle was taken as research object. The mesh of some gear teeth was divided, and the finite element simulation model was established to obtain the maximum contact stress of tooth surface under different load torque. The calculated value of tooth contact stress at the position of pitch point was acquired based on Hertz contact theory. The simulation model was verified by comparing the finite element simulation results with the theoretical numerical solutions. The TEs under different types of installation errors were simulated. The results show that all of the center distance error, axis intersection angle error and axis stagger angle error have effect on TE of gear pair. Taking the peak-to-peak value of TE as evaluation index, the analysis results of installation error contribution show that the influence of center distance error on transmission error is the weakest, and the influence of staggered angle error is the most significant, while the effect of shaft intersection angle error is between the two.

The video anomaly detection method based on fusion of attention with convolutional autoencoders was proposed to address the insufficient utilization of contextual semantic information in video sequences. The frame prediction strategy was adopted, and the convolutional autoencoder based on the Inception module was employed to extract the multi-scale features from input video sequences. To capture the interaction between moving objects and static backgrounds, the position attention and channel attention were incorporated. The memory enhancement module was integrated into the convolutional autoencoder to constrain generalization, and the latent loss function was introduced to enlarge the reconstruction errors of anomalous events. The calculation of anomaly scores was derived, and the dataset for anomaly detection was provided. To verify the proposed method, the qualitative analyses of abnormal behaviors, the model performance comparisons, the memory item update threshold experiments and the ablation studies were conducted. The results show that the proposed method can effectively detect the anomalies in videos with high detection accuracy. The AUC values on the UCSD Ped2, CUHK Avenue and ShanghaiTech datasets are 97.7%, 88.9% and 73.8%, respectively.

 To solve the problem of traditional Ethereum phishing scam identification and classification with high computational memory cost due to overlooking the importance of inter-subgraph relationships, the hierarchical graph attention framework was proposed to process subgraph classification tasks by leveraging graph attention technology for extracting behavioral patterns of account addresses. The hierarchical graph attention pooling encoder was constructed, and the node-level encoder was used to extract intra-subgraph node importance, while the subgraph-level encoder was employed to capture inter-subgraph significance for revealing potential associations both within and between subgraphs. Combined with graph contrastive learning techniques for joint training, the contrastive learning loss was introduced as regularize to alleviate label sparsity for improving subgraph classification performance. Comparative and ablation experiments were conducted on real Ethereum datasets, and the parameter analysis was carried out with F1 score as evaluation metric. The experimental results show that by the proposed method, the maximum improvement of 1.7 percentage points in F1 score is achieved on the real dataset with outperforming classical models of GCN, GraphSage and GAT, which requires less memory than other node classification approaches.

To quickly construct small sample dialect corpus dataset and improve the performance of Chinese dialect speech recognition, the dialect speech recognition method based on few-shot learning was investigated. Based on the selected high-quality public corpus, the dialect data set was constructed by extraction, correction, supplementation and recording. Combining the complementary characteristics of connectionist temporal classification network and attention architecture and the supplementary role of language model to end-to-end speech recognition model, the end-to-end speech recognition model based on hybrid CTC/Attention with additional language model was constructed. On this basis, the few-shot learning method based on data augmentation and transfer learning was used to complete the training of the dialect speech recognition model. The experiments were completed based on the Guanzhong dialect corpus. The results show that the fine-tuning speech recognition base model with character error rate of 4.9% can result in the dialect model with character error rate as low as 6.9% on the test set. The construction scheme of small sample dialect corpus is feasible, and the dialect speech recognition scheme based on small sample learning is effective. According to the cross-validation test, the character error rate of Mandarin in the test set of the fine-tuned dialect model is 27.2%, indicating that the fine-tuned model supports cross-lingual recognition. 

 To address the complexity, hazards and high testing costs associated with fault testing in aircraft anti-skid braking systems, the fault diagnosis method based on artificial bee colony (ABC) algorithm-optimized BP neural network was proposed. The simulation model of the aircraft anti-skid braking system was established in MATLAB/Simulink with incorporating dynamic airframe, wheel rotation, electro-hydraulic servo valve and braking device models. The typical failure modes of electro-hydraulic servo valve and wheel speed sensor were determined, and the fault injection module was constructed. The fault data samples were obtained by simulating the typical failures of wheel speed sensor and electro-hydraulic servo valve. The sliding window cropping method was employed to augment the sample data and construct fault dataset. The original and optimized BP neural networks were applied for fault diagnosis of the aircraft anti-skid braking system. The results show that the system with BP neural network optimized by the ABC algorithm achieves average fault diagnosis accuracy of 95.4% with 92.7% of original system and accuracy of 83.9% for wet runway sensor faults with 74.5% of original system. The proposed approach effectively enhances the fault diagnosis accuracy of aircraft anti-skid braking systems.

 Traditional vegetation analysis based on remote sensing imagery is often characterized by substantial manual intervention with low efficiency, suboptimal accuracy, slow updates and high resource consumption. To address these limitations, the novel fully convolutional YOLO (F-YOLO) network architecture was proposed. Derived from the YOLO framework, the target detection accuracy was improved by modifying the loss function to deliver pixel-level classification outcomes. The automated end-to-end pixel segmentation algorithm was designed to quantify the urban vegetation coverage. The Zijin Mountain area in Nanjing was selected as the study region. The images were processed through convolutional and pooling operations by the proposed F-YOLO to extract feature maps. These highly abstract features were subsequently upsampled to original dimensions through bilinear interpolation and followed by per-pixel vegetation classification for segmentation and coverage calculation. The experimental results show that by the proposed method, the vegetation segmentation accuracy of 94.76% is achieved with the vegetation coverage estimation precision of 96.72%. For object detection, the recall rate of 97.33%, the precision of 96.34% and the F1-score of 96.83 are significantly higher than those of SSD and Faster R-CNN. In terms of pixel-level segmentation, the accuracy of 94.76% also outperforms those of FCN-16, U-Net and FCN-32, which confirms the overall superior performance of the proposed approach.

In response to the national "double carbon" plan and to explore the potential of new carbon-free fuels in micro-power systems, the experiments were conducted to systematically investigate the flame characteristics of ammonia/hydrogen/air premixed flames in planar micro-combustor. The effects of operating parameters of flow rate and equivalence ratio on flame stability and combustor working performance were assessed. The formation mechanisms and transition laws of different flame structures were discussed in detail according to the internal flow heat transfer and external heat dissipation. The results show that as the flow rate is increased, the flame patterns appear repetitive extinction and ignition, planar, anchored, U-shaped and inclined flames in sequence. The velocity ranges of various types of stable and unstable flames are also determined. The flame stability at stoichiometric equivalent ratio is optimal when the equivalent ratio is 1.0, and the blowout limit can reach 0.35 m/s. The overall output characteristics of U-shaped flames are superior compared to other flame structures, and the maximum wall temperature can reach 900.0 K with the minimum wall temperature difference.

The internal flow characteristics of double-inlet nozzle are extremely complex and have significant impact on the cavitation occurrence capacity of jet. The distribution characteristics of jet velocity, the vapor-liquid two-phase and pressure during the generation process of artificially submerged cavitation jet were investigated, and the influence law of the outlet width of outer nozzle on the flow field was explored.The results show that when high-speed jet flows through the inner nozzle contraction section into the throat, the jet velocity is suddenly increased due to the contraction of the outflow section, and the high-speed low-pressure zone is formed at the throat and near the throat outlet. By the low-speed jet wrapping effect, the velocity distribution in the region eventually presents droplet shape, and the liquid phase in the region shows the phenomenon of first diversion with latter polymerization under the action of vapor phase. As the width of outer nozzle outlet is increased, the jet velocity attenuation is increased with decreased distribution range of vapor phase, and the maximum vapor phase volume fraction is decreased.

 Based on ABAQUS software, the secondary development of spatially mobilized plane(SMP)strength criteria was realized by external subroutine method. The saturated and unsaturated regions of slope were distinguished by Boolean variable, and the influence of soil moisture content variation on capillary cohesion, friction angle and soil unit weight was considered. The strength reduction method was employed to investigate the stability of three-dimensional unsaturated soil slope under different rainfall conditions. The pore water pressure distribution and the safety factor of slope were obtained. The results indicate that soil types and rainfall patterns influence the pore water pressure distribution and slope safety greatly. When the wetting front is disappeared, the impact degree from large to small is from delayed rainfall pattern, average rainfall pattern, normal rainfall pattern to advanced rainfall pattern. The slope stability is conservatively estimated when the three-dimensional effect and the middle principal stress are not considered in the slope stability analysis.

 To improve the performance of styrene-butadiene-styrene(SBS) modified bitumen under extremely high temperature climate and heavy load conditions, three kinds of composite additives of high modulus additive (HMA), Trinidad Lake asphalt (TLA) and Buton rock asphalt (BRA) were respectively used to prepare three types of composite modified hard bitumen specimens of HMA+SBS, TLA+SBS and BRA+SBS. The basic properties tests, the dynamic shear rheological test in frequency and temperature scanning modes and the multi-stress repetitive creep recovery test were carried out to evaluate the high temperature properties of the three kinds of composite modified hard bitumen, which were compared with the properties of SBS modified bitumen. The results show that HMA, TLA and BRA all can enhance the high temperature properties of SBS modified asphalt, and the reasonable contents are 20%, 25% and 8%, respectively. The construction temperature should be appropriately increased during applying different composite modified hard asphalt. HMA and TLA have good effect on the high temperature rheological properties of SBS modified asphalt, and HMA+SBS has the best high temperature rutting resistance under the low frequency condition. TLA+SBS has the best high temperature rutting resistance under the high frequency condition, while BRA+SBS has the best resistance to repetitive load and elastic recovery.