With the promotion of green development in civil aviation, aircraft noise has become an issue that cannot be ignored. An improved dynamic window approach (DWA) combining aircraft performance was proposed, which introduces the constraint of continuous climb operations (CCO) and constructs performance constraints for aircraft. To address the problem of rough solution set caused by traversal in traditional DWA algorithm, genetic algorithm(GA) was used for optimization. Secondly, speed was used to represent the time indirectly in order to optimize the track evaluation function. The effect of population distribution was added to make the model more reasonable. Finally, taking the departure direction of BOKIR-8T at Chengdu Shuangliu Airport as an example, the improved algorithm (DWA-GA) was compared with the traditional DWA algorithm, and the flight path under the influence of population distribution was compared, and the aircraft performance parameters and noise influence range were analyzed. The simulation results show that the improved algorithm is more accurate than the traditional DWA algorithm at low resolution, and the population distribution has obvious influence on the track.

In view of the current situation of unclear understanding of the genesis of the low resistivity oil layer in Guantao Formation of CFD6-4 oilfield in Bohai Sea, the microscopic and macroscopic genetic mechanism of the low resistivity oil layer was systematically analyzed by using clay mineral analysis, scanning electron microscope, particle size analysis, heavy mineral analysis, core nuclear magnetic resonance and other data combined with the study of sedimentary evolution. The research shows that the low resistivity oil layer is rich in clay minerals compared with the conventional resistivity oil layer. The illite mixed layer and illite layer are bridged to fill the pores to form a conductive network. The clay minerals distributed in the porous network fully contact with the formation water to produce cation exchange, forming the microgenesis of the low resistivity oil layer. The complex pore structure leads to high capillary bound water porosity, which also leads to lower oil saturation in low resistivity reservoirs, which constitutes another cause of formation of low resistivity reservoirs. Low-resistance oil reservoirs in the study area are mainly developed at the end of the half-cycle of the rise of the medium-term base level. On the whole, the river energy is weak, and the sand-carrying capacity is reduced. For example, the fine-grained sediment of clay minerals is gradually enriched, and the increase of the proportion of fine-grained sediment causes the complexity of the pore structure of the reservoir, and the increase of the bound water saturation, which constitutes the macro cause of the development of low-resistance oil reservoirs.

The emergence of aerial building machines has greatly improved the environment and efficiency of high-rise building operations, while also facing challenges such as increased difficulty in construction operations and complex construction processes. With the continuous deepening of digital transformation in the construction industry, the digital expression of building machine construction processes has emerged as an intuitive and clear solution. It significantly enhances the transparency of the construction process, optimizes resource allocation, and strengthens decision support for project management. An effective pathway for the digital expression of building machine construction processes was established, aimed at advancing high-rise construction towards intelligent management. Through theoretical foundations and field research analysis, the needs for the digital expression of building machine construction processes were identified, leading to the design of a framework for implementing digital expression of building machine construction processes. This research not only provides theoretical guidance for the digital transformation of building machine construction processes but also expands new perspectives on the application of knowledge graphs, interactive electronic technical manuals, model-based definition (MBD) techniques, and augmented reality(AR) technology in the construction field.

Considering the high-temperature thermosetting chemical issues in the manufacturing process of composite tensile armor layers, the curing kinetics of T700/epoxy prepregs were explored. Through differential scanning calorimetry (DSC) analysis and the Starink method, the autocatalytic reaction curing kinetic parameters were accurately calculated. Then a curing kinetic model was established. It has been shown by experimental results that the reaction rate of the prepreg is significantly increased at higher heating rates. After the peak is reached, the reaction rate is decreased more rapidly, resulting in a lower average final reaction heat. The apparent activation energy of the curing reaction for this prepreg is 77.04 kJ/mol, and high consistency with the experimental data is exhibited by the constructed curing kinetic model.

In order to determine the reasonable spacing of underground interchanges, a series of traffic simulation experiments with varying spacing cases was conducted in VISSIM using the Wenhui Street underground interchange of the Two Lakes Tunnel in Wuhan City as a practical case. The effects of interchange spacing, mainline and ramp traffic volumes and design speeds on crash risk at the diverging and merging areas of the underground interchanges were analyzed. Then, the crash risk variation at the diverging and merging areas of the underground interchanges was predicted using the four typical machine learning algorithms, i.e., extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and multilayer-perceptron (MLP). Results show that when the spacing increases from 1.5 km to 2.5 km, travel time, average delay, average queue length, and traffic conflict rate decrease significantly, and the collision risk index of time-to-collision(TTC) increase significantly. When the spacing is above 2.5 km, the decreases in travel time, average delay, average queue length, and traffic conflict rate start to slow down, and so does the increase in TTC. When the spacing is 2.5 km and above, the overall traffic operation efficiency and safety of the underground interchange increase significantly. The XGBoost model can predict the crash risk variation reaching a precision of 88.3%. This study can be a theoretical support and practical case for the setting of underground interchange spacing.

With the increasing number and aging of in-service oil and gas pipelines in China, issues such as corrosion, aging, and geometric deformation have become increasingly apparent. Due to the limitations of manual inspections, such as complex spatial environments and narrow pipe diameters, the use of pipeline robots for inspection and maintenance has emerged as a dominant trend in both domestic and international research. To address the risks of pipeline failure after prolonged service, understanding the latest advancements in pipeline robotics is crucial for setting forward-looking development goals and minimizing redundant research efforts. A comprehensive review of recent developments in in-service pipeline robotics was provided, these robots were divides into two main types based on their movement mechanisms and working environments: internal (passive and active) and external pipeline robots. By examining specific examples of each type, their overall performance were compared and highlighted key considerations for field applicability. Furthermore, the future directions were explored for pipeline robotics, emphasizing the importance of multi-parameter integration in ensuring the safe operation of oil and gas pipelines in the future.

In order to address the issue of coordinate base inconsistency in the fusion display of road building information modeling (BIM) models and tilted reality models within existing large-scale 3D geographic information system (GIS) platforms, a high-precision matching method for geographic coordinates between road BIM models and tilted reality models was proposed. Taking into account the distribution characteristics of road bands and the requirements for road maintenance, the model was initially segmented. Subsequently, a spatial distance-weighted least-squares coordinate matching parameter fitting method was developed based on the distribution of characteristic points on the road pavement and asset facility model, with a focus on accurately joining edges of the road pavement in each segment. Real road data was selected for conducting experiments to validate this coordinate matching method. The method effectively resolves bias issues in matching between the road model and tilted reality model, achieving accuracy at millimeter level post-matching, thereby meeting digital maintenance needs as well as dynamic updating requirements for road traffic facilities.

Fault diagnosis of industrial motor bearings is crucial for equipment performance and lifespan. Traditional diagnostic methods aggregate data from multiple factories, leading to issues with data privacy and high annotation costs. To address these problems, a fault diagnosis strategy based on adaptive local collaboration (ALC) federated learning was proposed. In this approach, bearing data under different working conditions was stored across multiple clients, with a central server collaborating with each client to build a federated learning diagnostic model. An improved ResNet-18 network was used as the classifier, which was trained within the personalized federated learning framework. The ALC federated learning method enables each client to effectively integrate global and local models, extracting global information to optimize local training results. Experiments demonstrate that this method enhances fault diagnosis accuracy while protecting data privacy, showing higher fault classification precision compared to other methods, especially in multi-factory environments.

Synthetic aperture radar (SAR) target recognition method based on deep networks requires a large amount of training data, and in practical applications, it is extremely difficult for SAR imaging systems to obtain sufficient and evenly distributed target data. One way to solve the small sample problem in SAR target recognition, is to use electromagnetic simulation technology to generate a large amount of SAR simulation data. However, there are still significant differences between simulated images and measured SAR images, so using simulation data directly cannot bring significant performance improvement for target recognition. A simulation data optimization method based on SAR target characteristic constraints was proposed to address the above issues. On the basis of analyzing the characteristics of SAR targets, a texture structure cycle-consistent generative adversarial network (TS-CycleGAN) based on texture structure and cycle consistency was constructed, in which the structural similarity measure was used to constrain the generation process of CycleGAN. This method can reduce the difference between simulation data and measured data, and can improve the usability of simulation data. The experimental results on the SAR SAMPLE dataset show that, compared to other simulation data optimization methods, the proposed method achieves significant improvements in image quality evaluation and classification performance.

Given the practical application background of installing underground pipelines in coal mine tunnels and the actual environmental conditions underground, a jointed tunnel pipeline installation robot was designed. The detailed design of the robotic arm structure was completed, along with its 3D modeling. The kinematic model of the robot was established, and MATLAB was employed to verify the forward and inverse kinematics of the robotic arm. Based on the established kinematic model, a trajectory planning algorithm for the Cartesian space of the robotic arm was designed, and MATLAB and ADAMS software were used to verify the robotic arm through simulation experiments. The simulation demonstrates that the structural design of the robotic arm is reasonable, and the trajectory planning scheme for the robotic arm is feasible.