Unique test requirements in civil large aircraft flight testing, characterized by short task durations, wide measurement point distribution, and numerous measurement locations, are addressed. Challenges in the existing wired Ethernet-based onboard data acquisition systems, including difficult measurement equipment installation, complex test cable layout, and prolonged retrofitting periods, are identified. A flexible, miniaturized, and compact space-compatible measurement system for critical wing structural state parameter measurements during civil aircraft flight testing was proposed. An integrated microsystem, including a flexible antenna module and a multi-sensor parameter collection module, was developed and integrated into the civil aircraft wing. The system's reliability and stable signal transmission were demonstrated. The design and application of a wireless flexible measurement system for wing state monitoring on civil aircraft were detailed. The system design approach, data transmission strategy, and integration with third-party loggers were described. Ground and flight tests were conducted to collect data.The onboard flexible system's capability to measure temperature, three-axis vibration, and pressure is verified.

The flooding cap is the key equipment for the pre-commissioning of subsea pipeline. Its deep sea installation operation has high risks and strict requirements, which puts forward higher requirements for the safety performance of structural strength. In addition to bearing loads during installation, the flooding cap also needs to block huge internal pressure in the pipeline during pressure test. Its structural strength and bearing capacity directly affect the safety and reliability of the whole subsea production system pre-commissioning. The flooding cap used in a 1 500 m deep gas field in the South China Sea Lingshui area was taken as the research object. Based on the relevant standards of DNV and NORSOK, elastoplastic finite element modeling of the flooding cap and key pressure components was carried out to analyze the safety strength requirements under different working conditions. The results show that the maximum Von Mises stress of each component of the flooding cap is less than the allowable stress under lifting conditions, and the high stress is mainly concentrated at the bolt connection. Under the impact condition, the flooding cap simulation can meet the installation speed requirement of 0.5 m/s, and the overall structural strength can meet the relevant standards. Under pressure testing conditions, the pressure capacity of the flooding cap is calculated to be 823 bar, and the measured pressure during the pre-commissioning operation of the Linsgshui gas field in the South China Sea is 268 bar, which is far less than its pressure capacity, satisfying the finite element calculation results. The relevant research results can provide theoretical basis and technical reference for the design and field application of flooding cap.

When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM (convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy.Compared to canonical correlation analysis (CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.

The construction decision of special geological area is very important and complicated. The uncertainty of geological conditions will directly affect the selection and implementation of construction scheme. In order to solve the non-equilibrium problem among the “five control” objectives (time limit, cost, quality, safety and environmental protection) of the bridge construction scheme in the complex special geological area of Southwest China, the network planning technology and BIM(building information modeling) were combined. BIM visualization technology, fuzzy set theory and GRA(grey correlation analysis) were integrated into the optimization of bridge construction schemes in complex special geological areas. HFMD(hybrid fuzzy multi-attribute decision-making model) based on duration-cost-quality-safety-environmental protection was established, and a visualization system of construction process was constructed by using Python and BIM technology. Assisted managers to make decisions, and the result met the “five control” index comprehensive optimization scheme, and the construction period was advanced by 10 days, and the cost was reduced by 3.1%, which proved the practicability and effectiveness of this model A and method. It provides a reference for the decision of bridge construction scheme in complex special geological area.

In order to accurately select the Copula function to simulate the mutual correlation between inclination and dip of jointed rock mass structural plane, the Copula function method to simulate the occurrence of jointed rock mass structural plane under different fitting indexes was proposed. The optimal Copula function was determined by using the least square Euclidian, AIC information criterion and BIC information criterion, and the optimal edge distribution type of the observed occurrence data of the structural plane was determined by Matlab software. At the same time, Monte Carlo sampling method was used to automatically generate simulation data, and the data was imported into Dips software for visualization processing, and the erP projection map of occurrence was obtained. The difference between the measured dip and inclination data and the simulated data determined by Copula function under different fitting indexes was compared. Finally, the validity of the method is tested based on engineering cases. The results show that different fitting indicators will produce different Copula functions, and there will be great differences in the effectiveness of simulation occurrence. Improper fitting indicators may lead to the selection of inaccurate Copula functions, so that the model can not accurately capture the relevant structure and features of the data. Inappropriate fitting indexes may lead to large errors between the fitting model and the real data, which will decrease the predictive ability and interpretation ability of the model. In this case, it is shown that the Gaussian Copula function selected under the fitting index of least square Euclidene values has the best fitting effect on the measured data. This research will help to select the appropriate fitting index when using Coupla function.

In the manufacturing process of wind turbine blades, gluing is an important part of ensuring the structural strength and tightness of the blades. In view of the low efficiency, uneven gluing, low quality of glue line and glue overflow on the surface of parts caused by the manual sealing and gluing operations commonly used in the gluing process of wind turbine blades, a kind of intelligent rubber shoe for wind turbine blade gluing with servo motor and reducer as driving components was developed, and the gluing process was introduced in detail. ANSYS analysis was carried out on the key components, and after simulation analysis, it was found that the structure of the intelligent rubber shoe could meet expectations. After experiments, it is verified that the shape of the rubber road is full, the two sides are smooth and flat, and the width and thickness of the rubber road meet expectations. The use of rubber shoes can not only reduce the work intensity of workers, but also reduce the use of 10% of the glue amount when gluing, and improve the efficiency and quality of gluing.

In order to apply remote sensing technology to uranium exploration in the Mouding area of Yunnan Province, based on ASTER (advanced spaceborne thermal emission and reflection radiometer) remote sensing data, the interference removal and PCA (principal component analysis) method was used to extract the Al-OH, Mg-OH, {\mathrm{CO}}_3^{2-} and iron-stained alteration information in the study area. The lineaments in the study area were automatically extracted by PCA and LINE model in PCI Geomatica software, and the density map of lineaments was created. Finally, combined with geological data, the relationship between uranium mineralization and alteration and linear structures in the study area was analyzed, and a favorable mineralization area was delineated. This study can provide some ideas for subsequent exploration in the area, and also provide some reference for remote sensing technology in mineral exploration in vegetation-covered areas.

In order to study the hydrothermal characteristics of the sand soil replacement subgrade, the method of indoor freeze-thaw cycle test was used to investigate the hydrothermal changes at different depths of the sand samples with different gradations, and to compare and analyze the effects of coarse sand and fine sand on the silty clay in the lower layer. The results show that during the freezing process, the soil close to the cold end will have a “water increase phenomenon”, and the silty clay is more prone to water migration than the sandy soil. Both the replacement of fine sand and the replacement of coarse sand can inhibit the cooling of the lower silty clay and reduce the freezing depth, so as to avoid the frost heave and thawing of the subgrade. Compared with coarse sand, the replacement of fine sand has a better effect on inhibiting the downward transmission of negative temperature at the top, and the replacement of fine sand is more conducive to maintaining temperature stability. The freezing depth of the replacement of fine sand is smaller than that of the replacement of coarse sand, which is more conducive to inhibiting the frost heave of the subgrade soil and maintaining the internal water stability. The freezing characteristic curve of soil and the thawing characteristic curve of soil do not coincide, and the freeze-thaw characteristic curve of replacement fine sand is least affected by temperature at the interface of replacement. It is concluded that the replacement of sand can effectively avoid the occurrence of frost heave phenomenon of subgrade, and the effect of replacing fine sand is better than that of coarse sand.

Traffic accidents pose significant risks to public safety and represent a critical issue in transportation systems. The accurate prediction of accident severity is essential for implementing effective prevention and intervention measures. An ensemble learning approach, combining the advanced algorithms XGBoost and MLP, was proposed to enhance the accuracy of traffic accident severity predictions. A stacked classifier was established and its performance in traffic accident prediction was thoroughly evaluated. The experimental results demonstrate that the integrated model significantly improves prediction accuracy compared to the traditional XGBoost model, with a notable 20.41% increase in the macro-average F₁ score. The advantages and innovations of the model, including model integration and network transformation, were highlighted. Additionally, the key features affecting the prediction results were analyzed, and the model's potential value in practical applications was explored. This study provides more scientific and efficient decision support for traffic safety management and is expected to play a crucial role in fields such as traffic management and intelligent driving.

Aiming at the problem of determining the effective design range of parameters during the optimization design of planetary gear transmission system, the dynamic sensitivity of two-stage planetary gear transmission system parameters was studied. Taking the two-stage planetary gear transmission system of vehicle as the research object, the natural vibration model of the system was established by Lagrange, the vibration modal characteristics of the two-stage planetary system were summarized, the expression of the vibration energy of the planetary system was deduced, the distribution state and the law of the vibration energy transfer in the same order were studied, and the mode change mechanism of the natural frequency of the system was further explored. The vibration energy transfer essential of the system natural frequency mode transition triggered by parameter change was analyzed. The parameter sensitivity equation of the natural frequency of the system is derived, the influence law of the modal transition phenomenon on the parameter sensitivity was studied, and the dynamic change law of the natural frequency sensitivity with the change of the parameter value was revealed. The method of dividing the parameter sensitivity interval based on the modal transition phenomenon was proposed, which realizes the effective guidance for the selection of the variable value range in the optimization design stage.