In recent years, spatial-temporal graph convolutional network (STGCN) has been introduced into traffic flow prediction, which has good spatial-temporal traffic data modeling ability and has achieved advanced performance, but there are still two problems: ①Traffic flow data have strong temporal and spatial correlation; ②Static pre-defined graphs are difficult to capture the spatio-temporal dependence of dynamic changes in traffic flow over time. To solve the above problems, a new spatial-temporal decomposed framework (STDF) was proposed, which used residual connection, forgetting gate and update gate to organically connect time module and space module to decompose and predict input information in multiple dimensions. In addition, by instantiating STDF, a new traffic prediction model based on input traffic signal decomposition decomposed dynamic spatial-temporal graph convolutional network (DDSTGCN) was proposed. It captured the spatiotemporal dependencies of traffic and designed a dynamic graph learning module that takes into account the dynamic nature of spatial dependencies. Finally, two real traffic flow data were used to compare with the existing traffic flow prediction algorithms. The experimental results show that the proposed method has good performance in the accuracy of traffic flow prediction and can effectively complete the traffic flow prediction in the real scenario.

In view of the problems of large lag and nonlinearity in aeration control systems for wastewater treatment. The principles of aeration control systems were analyzed, meanwhile, mathematical model for such systems was established. Based on traditional PID(proportion,integration,differential) control algorithms, particle swarm optimization algorithms, and fuzzy control algorithms, an improved particle swarm optimized fuzzy PID algorithm was proposed to overcome the drawbacks of expert-dependency and lack of dynamic performance in fuzzy PID control. The system was simulated using MATLAB to compare the speed, accuracy, and stability of the three control methods in terms of step response, disturbance rejection, and robustness under model mismatch conditions. The results indicate that the improved particle swarm optimized fuzzy PID algorithm outperforms traditional PID and fuzzy PID control algorithms in terms of step response, disturbance rejection, and robustness. It achieves faster and more stable regulation of dissolved oxygen, thereby enhancing control system performance. The improvement is expected to reduce operational costs at wastewater treatment plants, as well as improve system reliability and economic efficiency.

To enhance the classification accuracy of lower limb movements, this paper was introduced a hybrid recognition model based on surface electromyography (sEMG) that combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). Initially, sEMG data were collected from 20 subjects performing four types of gait movements: ascending stairs, descending stairs, walking, and squatting. Subsequently, the collected sEMG data underwent preprocessing, and both time domain and frequency domain features were extracted to serve as inputs for the machine learning recognition model. The CNN-LSTM model was then constructed for lower limb action recognition and compared against the performances of CNN, LSTM, and SVM (support vector machine,)models. The results demonstrate that the CNN-LSTM model outperforms the CNN, LSTM, and SVM models by 2.16%, 8.34%, and 11.16% in accuracy, respectively, thereby proving its superior classification performance. This model provides an effective solution for enhancing lower limb motor functions, offering significant benefits for rehabilitation medical equipment and power assist devices.

In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and address the limitation of acquiring limited information from a single sensor in grinding monitoring, this study a method for online recognition of the grinding direction of single crystal diamond tools based on multi-information fusion and particle swarm optimization (PSO) algorithm for optimizing the BP(back propagation) neural network was proposed. Vibration signals and acoustic emission (AE) signals were collected during the grinding process. The wavelet packet decomposition method was applied to analyze the vibration signals of the tool and identify the characteristic frequency bands strongly correlated with the grinding direction. The parameter analysis method was used to analyze the AE signals and extract the characteristic parameters. The energy values of the characteristic frequency bands in the vibration signals and the characteristic parameters of the AE signals were taken as the feature parameters for identifying the grinding direction of the tool. These feature parameters were then used as inputs to the BP neural network model for fusion and online recognition of the grinding direction. To overcome the disadvantage of the BP neural network easily getting stuck in local minima, the PSO algorithm was utilized to optimize the weights and thresholds of the neural network, effectively solving the problem of local minima. The experimental results show that the accuracy of online identification of the grinding direction of single crystal diamond tools is effectively improved by PSO-BP and multi-information fusion, reaching an accuracy of 85%, providing a new method for online identification of the grinding direction of single crystal diamond tools.

Efficient and accurate crack detection can provide a basis for assessing the structural safety of tunnels. Aiming at the shortcomings of traditional crack detection methods, which are complex and weak in generalization ability, an improved algorithm YOLOv5-CT(YOLOv5 CBAM Transformer) for tunnel lining crack detection was proposed.Considering the slender morphology of the cracks, the network introduced the Transformer module to improve the crack detection effect.The strong long-range dependency capture ability of the Transformer module enabled the proposed detection model to fully learn the contextual information of the crack region. In addition, the network integrated the convolutional attention mechanism CBAM(convolutional block attention module) in neck.The experiment shows that the YOLOv5-CT can achieve AP50 and AP of 85.2% and 51.3%, respectively, which is an improvement of 8.9% and 12.1% compared to the baseline model YOLOv5. It is better than other one-stage object detection networks in terms of accuracy, and the inference speed reaches 161.3 fps under 640×640 pixel conditions, which meets real-time detection of tunnel lining cracks.

To investigate the deformation characteristics of tunnels under impact and blast loads, A combination of model testing and numerical simulation was proposed to analyze the damage behavior and patterns under various dynamic loading conditions. The Hailuogou Tunnel was studied, and a combination of model testing and numerical simulation was utilized. Initially, an impact test was performed on the scaled-down physical tunnel model. Subsequently, numerical analysis of the tunnel model was conducted and verified. Then, a comparison was made between the tunnel deformation results of the scaled model and the prototype model under impact load. Finally, the effect of blasting load on the deformation of the prototype tunnel was analyzed. The results indicated that the proposed method accurately reflected the actual impact load’s effect on tunnel deformation. Additionally, the numerical analysis results of the scaled tunnel model closely matched the test results. Moreover, the deformation of the top of the prototype tunnel under the impact load was approximately 10 times greater than that of the scaled tunnel model, and it aligned well with the deformation caused by a blasting load equivalent to 500 kg of TNT. The impact load effectively simulated damage to the tunnel vault. Increasing the depth of cover and reducing the impact load represented effective measures to mitigate significant tunnel damage. The challenges of on-site testing during surface blasting are surmounted by this study’s findings. Additionally, a cost-effective, safe, and dependable testing approach is furnished for analyzing the destructive behaviors and modes of tunnels under various dynamic loads. Furthermore, technical support is offered for the safe and economical design of tunnels with optimized blasting loads.

With the development of the energy industry and the continuous growth of global energy demand, the exploration and development of geothermal resources have become increasingly difficult. Deep and ultra-deep geothermal resources have been identified as a key direction for the development of the new energy industry. As the drilling depth for geothermal wells continues to increase, the thermal and physical properties of the drilling fluid are found to have a more significant impact on the calculation of wellbore temperature and pressure amid changes in temperature and pressure. In A coupled numerical model of transient temperature and pressure in the wellbore during the drilling of geothermal wells was established, and the influence of the density and viscosity of drilling fluid on the calculation of wellbore temperature and pressure with changes in temperature and pressure during the drilling of geothermal wells was studied. It was shown by the calculation results that the viscosity and density of the drilling fluid significantly affected the calculation of wellbore temperature. When the changes in viscosity with temperature are considered, the calculation results of wellbore temperature are found to be 3.1% higher, and when changes in viscosity and density with both temperature and pressure are considered simultaneously, the results are 4.99% higher, compared with the case where changes in viscosity with temperature are not taken into account; To improve the accuracy of calculations, the thermal and physical properties of drilling fluid should be fully considered in calculating the temperature and pressure of geothermal wells.

In order to solve the problem of high effective inductor current and peak value in the quadrilateral inductor current control strategy of four-switch Buck-Boost (FSBB) converter, a boundary conduction mode (BCM) control strategy was proposed, which shortened the freewheeling phase without power transmission to zero in the existing quadrilateral inductor current control strategy, so as to reduce the RMS and peak value of inductor current. Firstly, the current waveforms of the FSBB converter in different modes of working modes and inductor currents were analyzed. Secondly, the constraints of the FSBB converter to achieve soft switching under all working conditions were analyzed, and the value rules of the inductor current are obtained. Then, the variation of inductor current in different modes was analyzed, and the control method in critical continuous mode was given, when the input and output voltage difference was small, increase the output power by increasing the duty cycle of the first or third stage, and when the input and output voltage difference was large, the FSBB converter works in the critical continuous state of inductor current, which effectively reduces the effective value and peak value of inductor current. Finally, a simulation model was built. The results show that the proposed BCM control strategy can achieve zero-voltage turn-on and has good dynamic response ability.

Under the condition of airport autonomous operation, perception of the operational environment is a crucial factor constraining the realization of autonomous airport operations. In the process of airport surface traffic operation, understanding the utilization of surface movement resources is a key step in establishing a comprehensive operational environment. The surface movement process at airports is first focused on in this study, and an ontology model for airport surface movement processes is constructed. Based on the structural layout of the airport surface road network, the movement paths were divided, and a "node-edge" model based on the connection between network nodes was established. Meanwhile, building upon the ontology model, dynamic and static attributes of the surface road network were defined as the basic properties of network nodes. With network nodes as the research object, various conflict scenarios existing in aircraft surface movement processes were modeled based on the dynamic attributes of network nodes, thus achieving a dynamic representation of aircraft movement processes at network nodes. Using speed data generated by aircraft dynamics models as a basis, a visualization representation of dynamic graphs of surface movement resource utilization in the presence of aircraft conflict scenarios was designed.Experimental results demonstrate that the model effectively represents both conflict and conflict-free scenarios in surface operations. This enhances the overall perception of surface movement resource utilization among participants in airport surface traffic.

To solve the issue of insufficient durability for steel bridge deck pavement, two types of double-layer stone mastic asphalt (SMA) pavement structures were used as research objects. Firstly, the most unfavorable loading position of the typical bridge deck was determined through the finite element analysis method; and the mechanical response of the above two structures at this loading position was calculated, thus the optimal structural combination for steel bridge deck pavement and its design index requirements were proposed. Secondly, two types of high viscosity and elasticity modified asphalt (A and B) were prepared; and then, taking the road performance of asphalt binders and their mixtures as the evaluation criteria, effects of asphalt binder’s types on the road performance of steel bridge deck pavement asphalt mixtures were compared, thus the asphalt binder with the best properties was selected. Finally, the bonding performance between the pavement layer and the steel plate was evaluated by using the indoor pull-out and oblique shear tests. Meanwhile, the bonding performance of the pavement layer under the most unfavorable temperature conditions was tested with the actual engineering. Test results show that the middle position is the most unfavorable load position on the steel bridge deck. Therefore, the tensile stress, vertical displacement, and bottom shear stress of the pavement layer at this location can be selected as the main design indicators for steel bridge deck pavement. In addition, the two designed pavement structures exhibit the consistent mechanical response patterns, among which the vertical displacement and layer bottom shear stress of structure 2 (SMA-13+SMA-10+asphalt mortar) are relatively smaller. As for the asphalt binders, comparing with SBS (styrene butadiene styrene triblock copolymer) modified asphalt, the prepared high viscosity and elasticity modified asphalt (A and B) have the better road properties, among which the road property of A modified asphalt is the best. The pull-out test results show that, under the temperature conditions of 25 ℃ and 60 ℃, the bonding strength between the pavement layer and the steel plate can all meet the design requirements. The actual engineering test result show that temperature inside the pavement structure layer exhibits the periodic variation pattern, with the highest temperature not exceeding 60 ℃. Therefore, the design index based on the interlayer bonding strength at this temperature is scientific and reasonable, and meanwhile, the interlayer bonding strength of various structural layers in the actual engineering meets the design requirements under this unfavorable temperatures.