In order to enhance the safety and efficiency of operations in the double-channel U-shaped apron area of large airports, an optimized operational procedure for the double-channel U-shaped area was studied. Firstly, the utilization and partitioning of taxiways in the double-channel U-shaped apron area were designed, and the positions of pushback holding points were optimized. Secondly, based on the partitioning of the double-channel U-shaped apron area and the optimized positions of pushback holding points, different operational procedures for aircraft were designed for various scenarios. Then, evaluation indicators were designed from the perspectives of safety and efficiency, and corresponding evaluation models were established. Finally, simulation experiments were conducted using Wuhan Tianhe Airport as the object. The results show that the proposed optimized operational procedure can reduce the total operation time by 13.3%, total waiting time by 31.4%, and waiting rate by 22.4%. The flight density was gradually increased until reaching the maximum theoretical capacity of the U-shaped apron area, and further verification was conducted. The results indicate that the proposed optimized operational procedure performs better across different indicators under varying flight volumes, verifying its effectiveness and providing theoretical references for current and future operational procedures of double-channel U-shaped apron areas.

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.

The rudder in the coaxial twin-rotor is a complex mechatronic position-following control system, and its control accuracy plays a key role in manipulating the flight attitude. Because the common rudder is lacking in adapting to the unique flight environment of the aircraft, the accuracy of tracking declination and the stability performance need to improve. The manipulation principle and structure of micro UAV were analyzed, the mathematical models of position loop, current loop double PID steering gear control system and transition position loop Fuzzy PID steering gear control system were established respectively. Combined with the actual flight conditions, the dynamic and static characteristics of the coaxial twin-rotor steering gear control system were analyzed by using Fuzzy editor and Simulink module. The results show that the rudder control system with current loop PID and position loop Fuzzy PID control has 28.6% less overshoot, 28% less adjustment time and faster response than the dual PID control system. Meanwhile, the Fuzzy PID parameters are adjusted in real time to track the changes, which can adapt to the complex and variable flight conditions of the coaxial twin-rotor more quickly. The obtained control system based on dual-loop Fuzzy PID shows high accuracy and meets the requirements of stable, accurate and robust working under complex working conditions, which is of great significance for the control system design of coaxial dual-rotor aircraft.

To mitigate the impact of highway accidents on traffic capacity and driving safety, a coordinated control strategy was proposed involving both service areas and toll stations. Firstly, the proposed coordinated control strategy was described in detail. Secondly, to simulate traffic flow more accurately under highway accident scenarios, the cellular automata model was enhanced by introducing different random deceleration probabilities, acceleration/deceleration rates, and lane-changing conditions for different vehicle types. Finally, the effectiveness of the proposed control strategy was validated through simulation. The results indicate that, compared to scenarios without control measures, implementing service area control can reduce average vehicle delay, fuel consumption, and cumulative carbon emissions by 62.90%, 69.50%, and 69.50% respectively. Moreover, using the coordinated control strategy of service areas and toll stations can further reduce these metrics by 55.76%, 59.58%, and 59.58% respectively. Precise control measures can significantly reduce the impact of accidents.

The offshore heavy oil thermal recovery platform has the characteristics of small space, high steam injection temperature, and high steam injection pressure, with temperatures up to 300 ℃. Once high-temperature and high-pressure steam leaks, it will cause serious consequences and pose a huge threat to equipment and inspection personnel. An effective steam leakage monitoring method was urgently needed. In order to solve these problems, the influence of thermodynamics, fluid mechanics and other factors were considered comprehensively to study the mechanism of steam leakage monitoring in offshore heavy oil thermal recovery. A virtual sensing monitoring method based on mechanism and inference was proposed, and for the first time, the indirect measurement method of steam leakage was applied to steam leakage monitoring in offshore heavy oil thermal recovery. A steam leakage monitoring model was built, and a hybrid sensing technology suitable for steam leakage monitoring in offshore heavy oil thermal recovery was formed for real-time online monitoring of steam leakage. The results show that this method can achieve leak discrimination and leak estimation based on operational data, and directly characterize the failure state of steam leaks online. The minimum detectable leak rate can reach 0.5%, and the accuracy of leak discrimination is above 96.49%. Compared with traditional methods, the minimum detectable leakage rate has increased by 90%, and the leakage discrimination rate has increased by at least 1.6%. This method solves the problems of limited installation of physical sensors on site, difficulty in obtaining effective monitoring data, and limited accuracy due to personnel experience, making up for the shortcomings of on-site monitoring methods for thermal recovery platforms and providing safety guarantees for offshore heavy oil development.

With the implementation of river dredging projects in China, a large amount of dredged silt has been generated. The treatment and disposal of silt have gradually attracted great attention. Using solidification technology is one of the effective ways to solve the problems caused by dredged silt. Taking the dredged silt from Beibaidang in Zhejiang as the research object, the solidified products were analyzed through X-ray diffraction (XRD), and the porosity and pore structure of the solidified silt soil were quantitatively analyzed by means of X-ray computed tomography (X-CT) and mercury intrusion porosimetry (MIP) tests. Meanwhile, it explores the mechanical variation laws and solidification mechanism of the soil under soaking and dry-wet cycling conditions. The research shows that the calcite content in the solidified soil increases with the increase of the solidifying agent dosage. The increase of ordinary sand dosage improves the small and medium-sized porosity inside the soil, but the overall porosity shows a decreasing trend. The results of water stability tests indicate that the strength and stability of the solidified silt soil are significantly improved with the increase of the solidifying agent content, and they first increase and then decrease with the increase of sand dosage.

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.

To investigate the impact of temperature on axial stress in large-span concrete variable-section continuous beam bridges under various wind speed fields, a method was proposed to calculate vertical temperature gradients separately based on inconsistent deck slab thickness and simulate lateral fluctuating wind speed fields using the spectral method. Firstly, vertical temperature gradient variations and their depths were calculated by employing the concrete heat-conduction equation, daily maximum and minimum temperatures, and deck slab thickness of the variable-section continuous beam bridge. Secondly, bridge modeling was performed using MIDAS Civil, and ZKH standard static live loads were simulated to represent moving train loads. Finally, static array wind loads and pulsating wind loads were applied to the bridge. The results indicate that the axial stresses in the left and right lanes obtained from the proposed method, which uses vertical temperature gradients and their depths derived from the concrete heat-conduction equation, daily temperature extremes, and bridge deck slab thickness, are larger compared to existing studies. Under the same wind speed field model, the harm to the bridge is greatest under gradient heating, followed by gradient cooling, while no temperature change results in the least impact. When the bridge is subject to the same temperature model, both the axial stress values and amplitudes of the bridge under pulsating wind loads are larger and more severe than those under no wind or static wind conditions, posing greater hazards to the bridge. The research findings can provide references for the structural design and safe operation of large-span concrete variable-section continuous beam bridges.

Occluded pedestrian re-identification is a challenging task in the field of computer vision. A method was proposed using the FGMS-Net network, which significantly enhances pedestrian re-identification in occluded environments through several improvements. Firstly, an improved foreground segmentation technique was employed to effectively remove background and other clutter information, resulting in more accurate feature extraction. Secondly, to address the occlusion issue, a multi-scale feature discrimination method was introduced, enabling the model to better capture local features and thereby enhancing identification capability. Finally, an attention mechanism was added to the backbone network, allowing the network to focus more on critical information and improve overall recognition performance. The experimental results show that method proposed has achieved significant performance improvement in the task of pedestrian re recognition with occlusion. On the Occluded-DukeMTMC dataset, the cumulative matching feature Rank-1 and mean average precision (mAP) reach 71.7% and 61.6%, respectively.

The super-large cross-section tunnel is prone to large deformation when passing through soft rock stratum. The reasonable selection of excavation method is of great significance for construction safety. In order to explore the applicability of the double-side nine-step excavation method to the construction of super-large cross-section tunnels, based on a 500 m² super-large cross-section soft rock tunnel under construction in Chongqing, the mechanical properties of sandy mudstone were revealed by laboratory experiments. The deformation characteristics of surface and super-large cross-section tunnel structures were compared and analyzed by numerical simulation and field monitoring. The excavation sequence, temporary support measures and excavation step length were optimized. The results show that the stress-strain curves of sandy mudstone samples under different confining pressures and different unloading rates are similar, and the triaxial compressive strength and deformation characteristics of rock samples change significantly. With the excavation of the core rock mass of the upper step, the displacement of the super-large section tunnel is abruptly changed. When the temporary support measures are removed, the deformation of the super-large section tunnel is further aggravated. Different excavation steps cause successive disturbance of surrounding rock, resulting in different unloading rates of surrounding rock and affecting the deformation of surface and tunnel structure. The temporary transverse bracing effectively limits the convergence of the arch waist, and the convergence of the arch waist is reduced by about 10.0 mm under all the layout conditions. In addition, the shorter the length of the excavation step, the smaller the deformation of the surface and the super large section tunnel.