Aiming at the thermoacoustic oscillation in the combustion process of a new generation of gas turbine, several time-domain analysis methods of high frequency data signals were retested and compared by constructing a complex network model. The results show that the two complex network models, node strength and network diameter, can give an earlier warning of thermoacoustic oscillations than the traditional time-domain analysis methods(root mean square and time kurtosis). Coupling prediction effect and data processing time, the node strength is preferred to construct the complex network model. Finally, the method was applied to the experimental data analysis of multi-nozzle micro-mixing burners, and it was found that the characteristic turning point of the method is about 2.3 s ahead of limit cycle analysis and the characteristic point of statistical analysis.

FDS(flocculation-dehydration-solidification) coupling process has been proven to significantly enhance the efficiency of resource conversion in engineering waste soil. However, the material fate within the process and the advantages of recycling press-filter filtrate remain to be further investigated. FDS experiments were conducted to analyze the material fate of each component in the flocculant-solidifying agent during the FDS process. Based on the findings, the potential benefits of recycling press-filter filtrate were explored. The results reveal that approximately 18% to 35.57% of Na⁺ and 0.1% to 0.56% of Si elements are detected in the press-filter filtrate, whereas Ca, Mg, and Al elements primarily remained in the filter cake, with proportions close to or equal to 100%. The recycling of highly alkaline press-filter filtrate into the process is found to not only improve the dissociation efficiency of mud and sand but also serve as a “pretreatment” for subsequent FDS stages. Waste soil particles are observed to adsorb residual materials from the filtrate, enabling dynamic adjustments in material dosage according to material transformation patterns and filter cake performance requirements. This approach ensures that materials lost in the filtrate are continuously recycled and utilized, maintaining a dynamic circular process.

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.

Recently, SSVEP(steady-state visual evoked potential-based BCI(brain-computer interface) researches have achieved significant development. However, the practical application of BCIs are still limited by several factors, one of which is the visual stimulus source. Most SSVEP-BCI systems rely on monitors, which are not portable and thus restrict the practical use in daily life. VR glasses, as wearable and portable devices, can provide realistic and immersive stimulus sources, which do not rely on monitors. Thus, they offer significant potentials for BCI applications. The VR(virtual reality) technology was introduced to display VR-SSVEP visual stimuli in 3D environment and enables subjects to immersively engage in BCI. The performance of 3D and 2D visual stimuli based on VR-SSVEP were compared in this study. The experimental results demonstrate that the performance of 3D visual stimuli is better than that of 2D visual stimuli. The average classification accuracy of 3D stimuli reaches 90.10%, which is 7.08% higher than 2D stimuli. Additionally, a 2-second stimulation duration achieves an optimal information transfer rate. This study confirms that 3D visual stimuli can effectively enhance SSVEP recognition performance, which indicates a practical use of the system and provides a novel approach for applying VR devices to the SSVEP paradigm.

The issue of distributed PV(photovoltaic) integration capacity allocation in distribution networks was addressed, which focuses on the photovoltaic integration capacity configuration based on load-storage coordination optimization. A coordinated regulation model for load-storage systems, incorporating energy storage, dispatchable load, and interruptible load, was first established. Based on this model, the constraints of load-storage regulation capabilities were considered. The optimization model for the configuration of distributed PV integration in the distribution network was developed with the objective of maximizing the capacity of distributed PV and the net investment and operational profit of the distribution network. Simulation results show that through the coordinated regulation of distributed PV with energy storage, dispatchable loads, and interruptible loads, significant improvements in the integration of distributed PV into the grid can be achieved.

In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating.The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.

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.

The effects of different diversion schemes on air flow and heat transfer in a countercurrent drying tower were studied by numerical simulation of maize drying process based on porous medium model. The influence of the angle box arrangement on the temperature field and velocity field in the tower was studied by numerical simulation and experiment. The results show that the cross arrangement of corner boxes can improve heat and mass transfer efficiency, reduce heat loss and solve the problem of uneven drying. At the same time, the increase of the inlet speed can also improve the uneven temperature distribution and improve the drying effect. It can be seen that the two key factors, the arrangement of the corner box and the inlet speed, should be fully considered in the design of the counter-current drying tower for corn drying. The optimization of drying tower structure can improve the overall drying efficiency and corn quality. The conclusion of this paper provides a useful reference for corn drying industry to reduce the cost and improve the quality of corn.

In order to guarantee the safety of UAV operation in low-altitude airspace and promote the rapid development of low-altitude economy, a detection method and resolution strategy for multi-UAV flight conflicts are constructed. Firstly, based on ADS-B(automatic dependent surveillance-broadcast) flight data, an improved FR-IMMCKF(fuzzy reasoning interactive multiple model cubature Kalman filter) algorithm was used to predict the UAV trajectory, and secondly, based on the relative motion status between UAVs, a preliminary screening of the conflict aircraft was carried out, and based on the velocity obstacle method, the vertical detection part was added so as to support the three-dimensional range of conflict detection, and then, the conflict coefficient was introduced as the weight in the flight conflict network, and the conflict status was proposed as the conflict status. Then, the conflict coefficients were introduced as the weights in the flight conflict network, and the conflict state SSM(space model) was proposed to visualize the resolution intervals, and finally, the resolution strategies of height adjustment, heading adjustment and speed adjustment were set up, and the optional resolution intervals of heading and speed were introduced. A low-altitude airspace five UAV flight conflict scenario was constructed for simulation and validation, and the results show that the proposed method is able to give a conflict resolution order and provide a feasible resolution strategy in a complex flight situation.

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.