Low level wind shear is an important factor affecting aircraft flight safety. Based on the observations from a three-dimensional scanning wind lidar at Baiyun Airport, Guangzhou during March 2023, the measurements from the wind lidar were preprocessed firstly. Then, TSSI (two-step identification method for wind shear) was proposed, which combined TDSI (two-dimensional synthetic wind shear identification) method with an adaptive window and the temporal wind shear identification method. The wind shear results recognized by the TSSI and TDSI methods were compared, and the evolutions of wind shear were analyzed. The main conclusions are as follows. Data preprocessing effectively removes isolated points and radial fluctuations observed by wind lidar, and fills in the missing data. The TSSI method is conducive to early warning of wind shear. During the observation period at Baiyun Airport, Guangzhou in March 2023, a total of 25 wind shear processes are identified by the TSSI. Among them, 21 cases are warned ahead of the TDSI, with an average warning time of 3~5 minutes, and TSSI also has a good alarm recognition function for both time and space dimension wind shear. Most of the identified wind shear processes occur around noon (e.g. 11:00-15:00) and last for about 15 minutes. The wind shear position is greatly influenced by the background wind field. The TSSI method proposed in this study can identify low-level wind shear earlier and more comprehensively, which is helpful to improve the accuracy of wind shear warning and provide guarantees for aircraft flight safety.

The flow pattern and its characteristics in the flood discharge stilling basin with step-down floors play dominant roles in effectively dissipating the discharged flooding water energy, guaranteeing the safety of the hydraulic structure and its downstream river bank stability as well as navigable flow conditions. A three-dimensional numerical model for water-air two-phase flows with strong nonlinearity, involving large free surface deformation and complicated solid-wall boundary conditions was established, and was applied to analyze flow characteristics in Xiangjiaba flood discharge stilling Basin with step-down floors. RANS(Reynolds-averaged Navier-Stokes) equations, Realizable k-ε turbulence model and VOF free surface tracking method were used in the developed numerical model. The model was firstly validated through comparisons of the simulated results and measured data for the flow patterns as well as the time-averaged pressure at the dam surface induced by the flood discharge jet. It is then applied to simulate the 3-D flow structure and fluctuation characteristics in the stilling basin with step-down floors induced by flood discharge from dam under the same discharge amount but different flood discharge scenarios. The results show that, for the flood discharge stilling basin with high and low step-down floors,under the same flood discharge condition, different scenarios of dam discharge gate open mode significantly affects the three-dimensional structure and characteristics of discharged flow in the dissipative pool induced by flood jets. The combined discharge from dam surface and middle holes results in the strong turbulent mixing of submerged multi-layer and multi-jet flows, effectively dissipating the energy of the discharged water and reducing the water surface fluctuations. The established numerical model can better reproduce the high-speed flooding jets and its turbulent motion in the dissipative pool associated with flood water discharge.

To address the issues of distortion and poor segmentation results in weld defect image segmentation, the crack and porosity welding defect images in the rim production process were taken as the research object. An improved particle swarm optimization algorithm based on simulated annealing is proposed for the three-threshold image segmentation of welding defects. First, a three-dimensional Otsu model is constructed using the grayscale value, average grayscale value, and median grayscale value of the image. Next, an adaptive inertia weight and asymmetric learning factor were introduced and integrated into the SA strategy to enhance the algorithm’s solving efficiency and ability to escape local optima. Finally, the SA-IPSO algorithm was used to optimize the three-dimensional Otsu model to obtain the optimal threshold and corresponding defect segmentation image. Various algorithms and models are employed to segment welding defect images. The results show that for crack and porosity defect images, the proposed improved algorithm outperforms the comparison algorithms in terms of peak signal-to-noise ratio and structural similarity evaluation metrics. The proposed method accelerates algorithm convergence while preventing distortion in segmentation results, thereby improving segmentation accuracy.

Bolts are the key to the stable connection of high-altitude equipment, but they are prone to abnormalities such as loosening under the influence of various factors, threatening the safety of the equipment. Currently, bolt detection methods based on deep learning are faced with the problems of class imbalance and label missing. Existing deep-learning-based bolt detection methods suffer from class imbalance and missing labels. A HDWL(historical dynamic weighted loss) model based on semi-supervised pseudo-label learning was proposed. By dynamic weighted orthogonality and class-adaptive fair punishment, the model classification was evaluated with historical data. Adaptive punishment was introduced to prevent overfitting and focus more on hard-to-classify samples, boosting model performance. Experiments showed that the HDWL model achieved significantly higher accuracy than other methods, with advantages in minority-class training and feature focus.

The soil covering of urban pipe jacking is shallow, and the surrounding pipelines are densely distributed and close to pipe jacking. In the process of pipe jacking construction, various factors may lead to gas pipeline fracture and gas leakage, resulting in gas, mainly gas, spreading in the soil and entering the pipe jacking, which brings potential great harm to the safety of pipe jacking construction. Based on the main-pipeline project of Maojiawan sewage treatment plant, the diffusion mechanism of gas in soil and pipe jacking and the construction ventilation technology were studied by theoretical analysis and numerical calculation. The results show as follows. With the increase of time, the diffusion flux of gas in soil first increases and then becomes stable. The farther away the pipe jacking is from the leakage hole, the lower the gas emission amount on the face, and the longer the time required for gas diffusion to the pipe jacking. The gas concentration on the opposite side of the air duct near the face of the pipe jacking is relatively high, and the gas is easy to gather. With the increase of air supply volume in the air duct, strong air flow is generated near the face, and the gas concentration near the face continues to decrease, but the rate of gas concentration reduction decreases. When the air supply volume in the air duct is 131 m³/min, the highest gas concentration in the pipe jacking is 4.57%, which is lower than the lower limit of gas explosion. With the increase of the distance between the air duct tuyere and the face, the maximum gas concentration in the pipe jacking first decreases and then increases. When the distance between the air duct tuyere and the face is 4 m, the maximum gas concentration in the pipe jacking reaches the minimum value of 4.41%. Based on this, a more reasonable ventilation parameter for pipe jacking construction is proposed.

To address the issue of fault signal transient characteristics being easily affected by noise, leading to misidentification of feeders in single-phase ground faults within resonant grounded systems, a line selection method was proposed that combines parameter-optimized VMD (variational mode decomposition) and improved D-S (dempster-shafer) evidence theory for fault feature fusion. First, to tackle the challenge of selecting the penalty factor Alpha and decomposition level K parameters in VMD, NRBO (Newton-Raphson-based optimizer) is introduced to adaptively determine these parameters under different noise environments. Next, three fault features—kurtosis, polarity, and transient energy—was fused, and the Jousselme distance was incorporated into D-S evidence theory to prevent conflicting results caused by noise interference on fault features. This approach provides the probability of fault occurrence on each feeder, allowing for accurate fault feeder identification. Finally, Simulink simulation results demonstrate that the method can accurately identify the fault feeder across various noise levels and fault scenarios. Compared to other parameter optimization algorithms, it achieves faster convergence, and the introduction of Jousselme distance further enhances the reliability of fault feeder identification.

To quantitatively evaluate the protective effect of the mangrove wave dissipating zone on the near coastline, based on the demonstration area of the Taishan Town Bay Mangrove Project in Guangdong Province, a combination method of on-site observation, theoretical analysis, model experiments, and numerical analysis was used to study the interaction process between the mangrove forests and waves. The verification and analysis of the wave dissipating characteristics of the mangrove forests were carried out from the perspectives of directional statistical characteristics and annual extreme value statistical characteristics, thus achieving a summary of the wave dissipating mechanism and statistical characteristics of the wave dissipating effect of the mangrove forests. The research results indicate that the basic theoretical system of vegetation wave dissipation based on the rigid column group flow theory has strong applicability to mangrove vegetation types, and the relevant wave dissipation theories can be adapted and integrated with numerical calculation models based on wave energy spectra. The simulation of vegetation wave dissipation process was carried out using physical model experiments, and the results were compared with the numerical simulation results using vegetation wave dissipation empirical formula, fully demonstrating the reliability of the vegetation wave dissipation empirical formula adopted in this study. The numerical simulation results show that mangrove forests can significantly reduce the frequency of large waves in various directions from a statistical perspective. The wave dissipation effect of mangrove forests increases significantly with the increase of wave height in different recurrence periods in the direction of strong waves. In the case of inconsistent normal wave direction and strong wave direction, the wave dissipation rate does not show a significant increase trend with the increase of wave height in different recurrence periods.

Dual-mode communication based on wireless and PLC (power line communication) can complement each other, and is widely used in smart metering and power Internet of Things. For the application of OFDM (orthogonal frequency division multiplexing) dual-mode communication system in new energy low-latency service access and other applications, an OFDM subcarrier diversity combination and power adaptive allocation algorithm considering the non-ideal channel estimation was proposed. Firstly, an adaptive optimal power allocation model was established with the constraints of service data volume and transmission power and the goal of minimizing the total delay. Then, on the basis of diversity grouping, whale optimization algorithm and threshold recovery were used to optimize power allocation, to achieve the compromise between algorithm complexity and performance. The simulation results show that the proposed algorithm can reduce the average transmission delay and stability while satisfying constraints such as rate, providing enhanced performance guarantees for real-time data acquisition in applications such as distribution equipment status monitoring.

Multi-object tracking is an important branch in the field of computer vision. Owing to the rapid development of computer hardware and deep learning technology, significant progress has been made in deep learning-based multi-object tracking, yielding remarkable results. To promote the research progress in the field of visual multi-object tracking, a comprehensive review of recent innovative outcomes was conducted to discuss the current state of research advancements.On the basis of introducing the background and application scenarios of multi-object tracking, the research progress was discussed in four aspects: tracking by detection,joint detecting and tracking,transformer-based tracking,referring multi-object tracking. Common benchmark datasets and evaluation metrics for multi-tracking algorithms were summarized, and a comparative analysis of the algorithms mentioned was conducted on these datasets. Ultimately, exploring the prospective evolution of deep learning-based visual multi-object tracking, three future research directions were proposed for scholars actively engaged in this field.

Relying on a deep foundation pit project of a metro station in a dual soil-rock stratum, the deformation patterns of the supporting structure and key sections of the foundation pit during different construction stages were obtained through on-site measured data. Based on the presence of adjacent buildings, a 3D numerical model for dual soil-rock deep foundation pits was established. After the excavation and dewatering construction of the deep foundation pit, the deformation and stress of the retaining structure on both the side adjacent to and away from the buildings, surface subsidence, and changes in the axial force of the supports were analyzed. On this basis, a sensitivity analysis was conducted on the influencing parameters of foundation pit deformation and stress, and the variation patterns were summarized and fitted. The study results indicate that the excavation of dual-element deep foundation pits in soil and rock exhibits significant spatial and temporal effects. In the time dimension, this is manifested as rapid development in the lateral displacement of pile bodies and surface settlement in soil layers, while the development rate slows in rock layers. In the spatial dimension, this is manifested as the corner effect of the pit. Changes in spacing and pile diameter essentially alter the overall stiffness of the retaining structure and the magnitude of external soil and water pressure borne by an individual pile. When the pile diameter is less than 1.0 m, the force sensitivity of deformation in the adjacent building side of the foundation pit significantly increases. The properties of rock and soil masses vary greatly at the soil-rock interface in a dual soil-rock formation, where over-excavation, support spacing, and changes in prestress are more pronounced. The research findings can provide valuable insights for similar soil-rock dual-element deep foundation pit engineering projects.