Wireless sensor network is composed of multiple micro-sensor nodes， and location technology is one of the important applications of WSN. At present， many location algorithms have high location accuracy in line of sight （LOS） environment， but poor location accuracy in non line of sight （NLOS） environment. An improved maximum entropy fuzzy probability data association algorithm based on arrival time was proposed. The grouping idea was utilized to divide N measure- ment values into L groups， and each group obtained the corresponding mobile node position estimation， model probability and covariance matrix through the interactive multi model （IMM） algorithm. Afterwards， the obtained L position estimation was subjected to non line of sight detection through a validation gate. The position estimation contaminated by non line of sight errors was discarded， and the corresponding correlation probabilities was used to weight the correct position estimates to obtain the final position estimation. Simulation and experimental results show that the proposed algorithm can reduce the influence of non line of sight errors and achieve higher location accuracy than the existing methods.

In order to solve the problems of high missed detection rate and low detection success rate in UAV small target detection， a small target detection algorithm based on YOLOv5 was proposed.Firstly， the swin transformer module was integrated into the backbone structure and the neck structure respectively， which improved the accuracy of target detection on the basis of reducing the computational cost， and could adapt to the detection of small target in UAV aerial photography.Secondly， the convolutional block attention module （CBAM） was introduced to enhance the network’s attention for small target features.Finally， the original loss function CIoU was replaced by the SIoU loss function， and the weights of high-quality samples were emphasized to accelerate convergence and improve the regression accuracy.Experimental results show that the detection accuracy on Visdrone2019 dataset is 35.3% after model optimization， which is 5.2% higher than that of YOLOv5.Compared with other classical and advanced algorithms，SWCBSI-YOLO algorithm performs well and meets the detection requirements of small targets for UAV aerial photography.

In order to improve the flutter velocity of a lifting surface with embedded mass，the lifting surface was simplified as a rectangular cantilever plate with concentrated mass.Based on the assumed mode method and the first-order piston theory，a flutter model was obtained，and flutter characteristics of the rectangular cantilever plate were investigated.Firstly，the structural dynamic model of the rectangular cantilever plate was established based on the Kirchhoff thin plate theory，and the modal properties of the plate with embedded mass were obtained using the assumed mode method； secondly，the first-order piston theory was introduced to approximate the modal aerodynamic force under supersonic conditions，and the time-domain expression of the flutter equation was obtained by substituting it into the structural dynamics model； finally，the flutter equation was transformed into the frequency domain by introducing the modal displacement in exponential form and the flutter velocities and flutter frequencies were solved by the p-k method.By changing the position，mass，number and other parameters of embedded objects，the law of their influence on the flutter characteristics of the supersonic cantilever plate was obtained.The results show that the flutter velocity of the lifting surface can be effectively increased by embedding small mass objects near the leading edge of the cantilever plate near the wing tip，and the flutter velocity is insensitive to the change of the embedded mass near the wing root.

The multi-depots vehicle routing problem with time windows （MDVRPTW） was studied. The MDVRPTW model was designed，and an adaptive large neighborhood search algorithm combined with the Gaussian mixture model （GMM） clustering algorithm was proposed. By classifying the customer set before the neighborhood transformation， the initial solution was optimized， and the algorithm efficiency was improved. Six different transformation factors were used. A scoring system was used to enable the algorithm to select appropriate transformation factor adaptively at different stages of the iteration. The rationality of parameter was analyzed and three simulation experiments were designed. The experimental results verifies the efficiency of the algorithm.

Tracing learners’mastery of knowledge is a pivotal research direction in the realm of wisdom education.Traditional deep knowledge tracing methods predominantly focus on recurrent neural networks，facing challenges such as the lack of interpretability and handling long sequence dependencies.Additionally，many methods overlook the influence of learner characteristics and exercise feature on experimental results.Addressing these issues，a cross-attention mechanism knowledge tracing model was proposed.The model integrated knowledge points and exercise feature information to obtain a question feature embedding module.Subsequently，improvements were made to the attention mechanism based on learner responses，resulting in a dual attention mechanism module.To account for real exercise-solving situations，a guess-error module based on attention mechanism was introduced.firstly，the model took in exercise features information，obtaining a learner response with integrating exercise information through the exercise features embedding module.Following processing by the guess-error module，authentic learner responses were derived.Finally，the prediction module yielded the probability of a learner answering correctly in the next instance.Experimental results demonstrate that the cross-attention knowledge tracing model，incorporating exercise features，outperform the traditional deep knowledge tracing （DKT） model，with 3.13% increase in AUC and 3.44% increase in ACC.This model proves effective in handling long sequence dependencies while exhibiting enhanced interpretability and predictive performance.

Aiming at the current target detection methods based on deep learning for transmission line，the feature extraction ability is poor for small target，easy to misdetection leakage detection，detection accuracy is low， detection speed is slow.A transmission line target detection method was proposed based on an improved neural network model YOLOv7.Firstly，the MobileNetV2 network was used as the feature extraction part of YOLOv7 to achieve lightweight processing of the model.Secondly，the CA mechanism and ASPP module were introduced to improve the accuracy and perception of the model.Finally，the self-drawn transmission line obstacle data set was used for training improved YOLOv7 network and compared with the original YOLOv7 model.The results show that the algorithm proposed has significantly improved the accuracy and recall rate，which meets the fault detection in complex scenarios and is more conducive to model deployment of mobile devices and embedded systems.

Thermal contact resistance between aircraft components is unavoidably.It is very important to acquire thermal contact resistance accurately for detailed design of thermal structure.To overcome the measurement difficulty under high temperature and high pressure，a measuring device was independently developed based on static heat flow method.The device can effectively obtain the thermal contact resistance between solid structures under given interface pressure and hot surface temperature up to 1 500 ℃.By using this device，three kinds of thermal contact resistance tests were carried out successfully.The influence law of pressure，temperature and interface roughness on the thermal contact resistance were figured out.Experimental results show that the measurement device is stable and reliable with uncomplicated operation，while the simulation accuracy for temperature and pressure load is satisfied.This device can provide supports for the development and engineering application of aircraft thermal structures.

In order to meet the requirements of hailstone ingestion test verification for domestic turbofan engine and obtain effective hailstone ingestion capability verification，according to the relevant requirements of hailstone ingestion test in GJB241A-2010 and CCAR-33R2 standards，the design method of hailstone ingestion test scheme of turbofan engine was studied，and the experience of the design process was summarized.Based on the analysis of the connotation of standards related to hailstone ingestion tests and practical application scenarios，the design methods of the test parameters，such as hailstone quantity，hailstone speed，engine working state，and impact position were studied.By studying the hailstone projection scheme in relevant foreign tests，the design and evaluation method of the hailstone projection scheme was determined and the corresponding simulation analysis method was defined.The test scheme designed according to the method was verified on a certain engine，which can provide reference and guidance for the design of the hailstone ingestion test scheme of the turbofan engine.

Considering that the causal mechanism of aviation accidents is complicated and has many causal factors with strong gray characteristics， the traditional gray prediction model is only applicable to univariate prediction and has the defect of low prediction accuracy.A method of aviation safety prediction was proposed based on a multivariate gray model optimized by genetic algorithm. Firstly， the analysis method of fishbone diagram was applied from the perspective of SHEL model to determine the factors affecting aviation safety， and the correlation coefficient matrix visualization graph was used to further screen the key causative factors. Secondly， a multivariate gray aviation safety prediction model was constructed with human factors， environmental factors， equipment and facility factors， external influencing factors and as the strong input indexes of the prediction model， and the optimal solution of the model’s undetermined parameter r was searched globally and parallel by genetic algorithm. Finally， simulation experiments were conducted utilizing Chinese civil aircraft accident rate of 10 000 and aviation unsafe event statistics from 2007 to 2016. Predictive comparisons were then made between two gray prediction models， GM（1，1）and MGM（1，n）. The findings indicate that compared to the traditional gray model， the proposed method demonstrates an average prediction error of around 1.6% in the aviation safety short-time prediction， showcasing the effectiveness and high accuracy of the proposed method.

In response to the crack at the installation side of a certain engine pipeline bracket， the research direction for solving the failure problem was clarified through analysis of the fracture at the bracket crack and the static strength stress of the bracket components. From aspects of part design， processing and assembly， the cause of the problem was determined to be the interaction stress between the bracket and the pipeline， which made the bracket work under original vibration stress and additional stress introduced during assembly. Due to the strong rigidity of the bracket components and weak buffering under stress load， the stress generated from bracket elastic deformation would be transmitted to the hole edge， probably causing the formation of cracks. The improvement with floating structure was designed which has the ability of axial deformation compensation and can significantly reduce the stress transmission. The proposed optimization design is validated by tests.

Using the carbon fiber-reinforced composite tank can remarkably reduce the weight of the launch vehicle.However，the analysis method for cryogenic tanks subjected to mechanical-thermal loads remains to be studied，especially to accurately consider the microthermal stress produced between fiber and matrix in the cryogenic environment.A representative volume model containing multiple fibers was adopted，combined with the matrix and fiber failure criteria，to establish a microscopic stress field and failure prediction model.The k-means clustering method was used for dimensionality reduction calculation，and an efficient and high-fidelity trans-scale analysis method for composite tanks was proposed.The results of illustrative examples show that the proposed method can accurately predict the elastic constants and failure strength of the composite single-layer plate according to the thermal and mechanical constants of fiber and matrix.The leakage failure process of a composite tank subjected to mechanical-thermal loads was simulated，and the critical load and failure state were given.

To study the evacuation characteristics in the Airbus 330 cabin crew under different hatch opening conditions，the Pathfinder software was used to simulate the evacuation characteristics.The evacuation characteristics of personnel were explored in four situations： all cabin doors opened， emergency doors could not be opened， front cabin doors could not be opened， and rear cabin doors could not be opened.The results show that the simulation time for evacuation is 63.3 s when all hatches are open，while 118.3 s when the emergency hatch cannot be opened.The per-unit-time flow of people at No.3，No.4，No.7，and No.8 hatches decreases by 0.09 people，0.2 people 0.09 people，0.04 people respectively.It takes 64s to evacuate people when the front cabin door could not be opened.The number of people evacuated from No.3，No.4，No.5，and No.6 doors are 47，48，52，and 50 respectively，indicating that the failure to open the front cabin door does not have no significant impact on the evacuation time.When the rear cabin door could not be opened，the evacuation time is 112.3s.The time interval from the first person to the last person from the No.3，No.4，No.5 and No.6 doors is 67.7 s，67.6 s，109.9 s and 107.1 s，respectively.This indicates that more passengers in the economy class choose to escape from the hatches No.5 and No.6，which increases the evacuation time.The research results provide a reference for understanding the evacuation characteristics of personnel under different hatch opening conditions.

Human action recognition is a key technology for understanding pedestrian intentions from video captured by unmanned aerial vehicles （UAV）.However，UAV platforms have limited computing power，and existing action recognition methods are inefficient.A lightweight spatial grouping attention graph convolutional network （SGA-GCN） was proposed to reduce network depth to improve the efficiency and ensure the accuracy of action recognition.In order to capture body parts that represent global motion，spatial grouping attention was introduced to enhance local features with high similarity to global features.Moreover，since it was impossible to effectively distinguish actions with similar motion trajectories solely based on joint and skeletal features，a high-order feature encoding of skeletal angles was constructed to capture changes in angles between limb joints that better reflected subtle motion differences and improved feature representation capabilities.Finally，to address the low frame rate issue in UAV aerial video，a linear interpolation scheme based on inter-frame differences was proposed to increase sample information quantity.Experimental results demonstrate that compared to the existing state-of-the-art （SOTA） methods，the proposed approach achieves better performance in terms of recognition rate，parameter quantity，training time and execution time on the UAV-Human dataset.

Based on the data of A-share listed enterprises in Chinese equipment manufacturing industry from 2011 to 2021，the mechanism of digital transformation and dynamic capability on enterprise innovation performance were empirically tested.The results show that digital transformation can promote the innovation performance of equipment manufacturing enterprises by enhancing dynamic capabilities，and organizational inertia has a different degree of moderating effect between digital transformation and dynamic capabilities.The analysis of enterprise heterogeneity shows that the impact of digital transformation on enterprise innovation performance is significantly different between state-owned enterprises and non-state-owned enterprises，as well as among the eastern，central and western regions.The research conclusions remedy the theoretical gap between dynamic capabilities and organizational inertia，and provide practical guidance for the internal mechanism of digital transformation to promote enterprise innovation performance.

Based on the NNW-PHengLEI software，for surfaces with uniformly distributed rough elements，rough surface boundary condition corrections and transition momentum thickness Reynolds numbers were introduced into the k-ω SST turbulence model and the γ-Re_θt transition model respectively.At the same time，some rough surface settings were added to achieve local/global rough surface boundary layer flow simulation capability.Test results show that when an airfoil is in a fully turbulent state，rough surfaces can reduce the stall angle of attack by 4°，and an equivalent roughness height of 1×10^-3 of chord length can cause a decrease in maximum lift coefficient by approximately 36%.Additionally，rough surfaces can advance the boundary layer transition，leading to an increase in skin friction drag.The computational analysis proves that the rough surface boundary condition proposed by Hellsten et al.can reflect the roughness effect more accurately than the dissipation rate boundary condition.The computational aerodynamic forces are consistent with the experimental data，and the flow field simulation results conform to the flow characteristics of rough surfaces.In addition，Hellsten’s model makes it possible to ensure stable coupling simulation with the transition model and its rough surface correlation function.The simulation results for the transition onset location and its trend with roughness level and freestream turbulent intensity are consistent with the experimental data.Still，the transition length needs further verification and correction through more rigorous experiments.

The changes in stiffness and damping of the bolted joint surface can cause the changes of dynamic characteristics of the whole bolted joint structure，so it is of great practical value to accurately obtain the dynamic parameters of the joint surface in engineering.Based on the bolted structure，an improved equivalent model of the stiffness of the joint surface with uneven distribution in the range of bolt preload was proposed，and the distribution of different stiffness matrix elements in the finite element modeling was analyzed.The stiffness parameter identification of bolted joint surface was carried out by combining experiment and finite element analysis method.The results show that the increase of the number of stiffness matrix elements can improve the accuracy of natural frequency solutions to some extent.At the same time，considering the uneven stiffness distribution of the joint surface in the range of bolt preload，the equivalent modeling accuracy of the bolted joint structure can be effectively improved.

The strength degradation law of composite materials is very important for studying the structural fatigue life. However， some parameters involved in existing residual strength models are confirmed using residual strength test data， so the cost of model construction is high. By exploring the relationship between fatigue life and residual strength of composite materials， taking the cumulative distribution function of fatigue life as the starting point， a damage degree with clear physical meaning was constructed，and then an improved residual strength model was proposed. The improved residual strength model didn’t need residual strength test data but only initial static strength and fatigue life data. A fatigue life prediction model called S-N-φ model was constructed based on the improved residual strength model， which can consider the influence of initial static strength on fatigue life. The results show that the prediction accuracy of the improved residual strength model is satisfactory， and the S-N-φ model has better prediction accuracy than the classical S-N curve model.

Addressing the issue that the current energy method cannot accurately describe the time-varying meshing stiffness of the gears with tooth flank spalling，resulting in the imperfect dynamic modelling method for the fault of gear transmission system，an improved energy method was proposed.Considering various types of tooth frank spalling，a time-varying meshing stiffness formula that consi-dered the tooth frank spalling was established.The established time varying meshing stiffness was introduced into a six-degree-of-freedom transmission system，creating a dynamic model of the gear transmission system that could simulate the tooth frank spalling.The Newmark-β method was used for solving，the numerical simulation results were compared and analyzed with the experimental data to verify the accuracy of the established model.Based on this model，the influence of the main parameters of tooth frank spalling on the time-varying stiffness，the corresponding stiffness variation law and the frequency characteristics of the dynamic response were investigated.The results show that the secondary frequency peak with a spacing of 10 Hz occurs on both sides of the characteristic frequency of the gear transmission system with tooth frank spalling.

In order to carry out fault diagnosis with only health status data，an optimized swin transformer deep neural network architecture was constructed to extract and reconstruct health data features，which proposed an unsupervised learning method for rolling bearing fault diagnosis. Compared with autoencoder，depth encoder，convolutional autoencoder，and sparse autoencoder，the accuracy is 98.62%，76.46%，68.69%，77.69%，68.00%，respectively，which is more than 20% higher than the accuracy of comparison network.

The damage mechanism of fiber reinforced resin matrix composites is complex.To ensure long-term stable application，advanced health monitoring technology must be used to monitor the damage.A sensor based on carbon nanopaper can sensitively monitor resistance changes and impact damage on carbon fiber reinforced polymer （CFRP）composite.A damage monitoring system based on an artificial neural network （ANN） deep-learning algorithm was designed.Through data analysis，the system could effectively monitor the occurrence and location of CFRP damage for a long time，and the damage location accuracy was as high as 92%.It can be inferred that the damage monitoring system can evaluate the health status of composite materials.

Based on the transformer architecture，a knowledge tracing prediction model for learning trajectory was proposed， which solved the following problems in the field of knowledge tracing using the transformer architecture： the model lacked the learning of knowledge point information； the attention scores in the self-attention mechanism showed a long-tail distribution and required square computatio-nal overhead； the prediction strategy of the model lacked consideration of learners’ability. In the data preprocessing stage， LTKT used the knowledge integration mechanism in the field of education to integrate multiple knowledge points involved in the subject， and the integrated knowledge formed was used as input to the model along with other learning trajectory information； LTKT introduced a sparse self-attention mechanism according to the characteristics of the long-tail distribution of attention scores into the encoder and decoder structure， and embedded a position encoding containing absolute distance and relative distance in it， so that the deep attention mechanism could also learn the position relationship between topics. In the prediction strategy， LTKT used the bilinear layer to fuse the learning ability features extracted by the learning ability extraction module and the output of the decoder to comprehensively predict the student's answer performance at the next moment. Experiments were carried out on two real large public datasets， and compared with other excellent models. The results show that LTKT has significantly improved the AUC.

To address the drawback of traditional painting trajectory planning algorithm requiring extensive searching for nearest point pairs，a initial trajectory point extraction algorithm based on the point cloud thin-slicing technology was proposed.This algorithm determined slice thickness by calcula-ting the density of the point cloud model and designated points on the slice as initial trajectory points， thus improving operational efficiency by avoiding point pair searches. To tackle issues such as insufficient trajectory precision and excessive curvature caused by traditional linear interpolation algorithm， a trajectory fitting algorithm was introduced. This algorithm firstly maped trajectory points in three-dimensional space to a two-dimensional coordinate system and utilized the isolation forest algorithm to eliminate outlier trajectory points，followed by combining curve fitting methods for trajectory planning. Experimental results demonstrate that the trajectories planned by this algorithm exhibit higher precision and smoothness， while also enhancing the overall efficiency of the algorithm.

A humidity influence of air thermophysical properties on the turbofan engine performance model was considered to study the influence of atmospheric humidity on turbofan engine performance. The influences of atmospheric temperature and humidity on engine thrust， exhaust temperature and fuel flow were calculated and analyzed， and the numerical humidity correction curves were compared with the experimental curves. The results show that the engine thrust， exhaust temperature and fuel flow gradually decrease as the relative humidity increases at the same atmospheric temperature and the influence of relative humidity is more significant when the atmospheric temperature is higher.The humidity correction coefficients of engine thrust，exhaust temperature and fuel flow linearly correlate with the humidity ratio，consistent with experimental results. But when the atmospheric temperature is below 0 ℃， the influence of humidity on engine performance may not be considered in practical applications.

In order to solve the problem that the classical K-means clustering algorithm reguired users to know the number of clusters in advance and the clustering results were sensitive to initialization of the algorithm， a comprehensive scheme was proposed to improve the random initial partitioning of K-means algorithm and visually determine the number of clusters. Firstly， the data was standardized to make it obey normal distribution， and the most important features were extracted by principal component analysis to achieve dimensionality reduction of high-dimensional data. Then， the farthest centroid selection and min-max distance rule were used to modify the random initialization of K-means algorithm to avoid empty clusters and ensure data separability. Based on these， the statistical empirical rule was used to estimate the range of the number of clusters， and the optimal number of clusters was assessed by searching the elbow of sum-of-squared-error curve within this range. Finally， by calculating and comparing the silhouette coefficients of each cluster， the clustering quality of the algorithm was evaluated， thereby ultimately determining the inherent number of clusters in the data. The simulation results show that the proposed scheme can not only visually determine the potential number of clusters in the data， but also provide an effective method for high-dimensional data analysis in the era of big data.

In engine performance analysis，the ratio of the cooling air flow of high-pressure turbine guide vane to compressor inlet flow is a fixed value.However，the actual bleed air flow of the turbine is affected by some factors，such as the pressure difference between the inlet and outlet of the cooling air flow path，flow area and flow resistance，resulting in deviations between the current simulation accuracy and actual performance of the engine.To further improve the simulation accuracy of engine performance，a modeling method based on the flow characteristics of high-pressure turbine guide vane was proposed.The computational model of the core engine performance was improved based on this method，and the performance parameters of one core engine were calculated with this model.The numerical results show that，for this core engine the actual cooling air flow of turbines decreases after considering the flow characteristics of high-pressure turbine guide vane，and the cooling air flow is more sensitive to the change of the total pressure recovery coefficient of the cooling air flow path.In the performance calculation of the core engine，after using the cooling air flow correction of high-pressure turbine guide vane，the total temperature at the outlet of the combustion chamber decreases by 1% to 2%，the temperature ratio of the core engine increases by 0.2% to 0.45%，the unit cycle power increases by 0.24% to 0.48%，and the pressure ratio of the core engine and the fuel consumption rate per unit cycle power change relatively little.

Rocket image target recognition and tracking is an important application field of image target recognition，which is an important support for rocket test launch and flight control，and has great significance for rocket target tracking and attitude analysis and control.Image tracking of rocket target in ascending stage is an important stage of rocket flight measurement and control，but at present，the video image tracking of rocket in ascending stage mainly relies on manual operation of pinion controller to achieve rocket tracking in the image.The tracking image has some phenomena such as tracking lag and picture shaking，and the tracking effect is greatly affected by human factors.Combining full convolution theory with deep learning method，a method of rocket image target recognition and tracking based on full convolution deep neural network was proposed.Images of rocket launch and ascending flight were collected as samples，and a full convolutional network model was constructed and trained.An end-to-end semantic segmentation method was adopted to realize semantic judgment of rocket targets at pixel level on the basis of deep classification network，with good recognition rate and robustness.Based on the recognition of the rocket target，the PTZ control model was established，the high-quality image of the rocket ascent stage was obtained through the intelligent control of the PTZ，and the tracking of the rocket target was realized.

According to the CCAR-33R2 standard 33.78（a）， a test method of the engine hailstone ingestion was studied and verified to investigate the hailstone ingestion capability of one turbofan engine. The test process was arranged； the test conditions were determined； the engine and test bench were analyzed； the protection， photography， and projection devices were designed； and the producing method of hailstone was studied. The hailstone was projected to the engine by the projection device. At the same time， a target calibration test and an air gun test were designed before the actual test. The target calibration test ensured the speed and accuracy of the projection， while the air gun test tested the influence of the projection device on the engine intake. Furthermore， a high-speed photography test was conducted to verify the speed and impact accuracy of the hailstone. The engine thrust decreased for a short time during the process of hailstone ingestion and then resumed stable operation. After hailstone ingestion， the exhaust temperature of the engine decreased by about 2%～3%， the rotor difference changed by about -0.6%～0.2%， the thrust decreased by about 0.1%～1.6%， and other parameters did not have significant change. The impact response of the fan was collected through the time-domain signal of the low-pressure measuring point. After disassembling the engine， the deformation of the intake edge of 5 fan blades was checked， no block falling occurred， and no structural damage that may endanger flight safety was found， which verified the airworthiness conformance of the turbofan engine for hailstone ingestion.

In view of the problems of difficulty in extracting gear fault features and low diagnosis accuracy under noisy environment，a gear fault diagnosis method was proposed，which combined VMD parameter optimization based on comprehensive evaluation indicators，KPCA feature fusion and BP network.Firstly，in order to effectively evaluate the IMF components after VMD decomposition and avoid the problem of manually setting relevant parameters for VMD，a comprehensive evaluation index based on envelope entropy and kurtosis was designed to establish a fitness function for VMD parameter optimization and screen the optimal IMF components.Secondly，after performing VMD decomposition according to the optimal parameters，a multi domain feature set was extracted from the optimal IMF component，and then the KPCA model was used to fuse its features.Finally，fault diagnosis was performed using the BP network model.The experiment shows that under the same experimental conditions，compared with other traditional methods，this method improves the recognition rate of gear faults，with an accuracy of up to 98%，proving the effectiveness of this method.

In the complex and changing market environment， high-end equipment manufacturing industry must enhance technical strength and improve innovation effectiveness through collaborative innovation， of which partner selection is a key step to achieve innovation. Firstly， by analyzing and summarizing relevant literatures on partner selection evaluation indicators， and combining with the colla-borative innovation development needs of high-end equipment manufacturing industry，the collaborative innovation partner selection index system of high-end equipment manufacturing industry was estab-lished from seven dimensions： complementarity， compatibility， innovation resources， innovation ability，innovation environment， reputation and trust as well as technical level. Then，considering the complexity of the high-end equipment manufacturing industry and evaluation fuzziness，a group decision-making model of EDAS was constructed based on possibility under the probabilistic hesitancy fuzzy environment，and applied it to the collaborative innovation partner selection under indicator system. Finally，a numerical example was used to verify the research method，which provides a certain reference for the collaborative innovation partner selection of high-end equipment manufacturing industry.

As a navigation solution with higher accuracy and better robustness compared to single navigation systems，GNSS/INS integrated navigation has been widely applied in various carriers.Aiming at the problem of the satellite navigation signal interruption caused by environmental obstruction or electromagnetic interference to reduce the accuracy of integrated positioning，GNSS/INS integrated navigation positioning method by a fully connected neural network （FCNN）was proposed.This method consisted of training and prediction modules.Under normal GNSS signal conditions，the me-thod utilized the position and velocity information calculated by INS and the position and velocity information output by the integrated navigation to train the FCNN model.When the GNSS signals were interrupted or fail，the pre-trained FCNN model was used to predict the navigation solutions.Experimental data was employed to validate the proposed method.The results indicate that the GNSS/INS based on FCNN integrated navigation method proposed in this study effectively suppresses the divergence of single INS positioning errors，thereby improving the accuracy and availability of GNSS/INS integra-ted positioning results when the GNSS signals are interrupted.

The unmanned aerial vehicle with folding wing can serve in multiple mission profiles，the flight environment is complex and susceptible to low-altitude gusts. A new parameterized aeroelastic model with non-intrusive characteristics was developed to quickly calculate discrete gust response cha-racteristics of Z-shaped folding wing at different folding angles. Firstly，structural and aerodynamic enhancement design was made to the existing Z-shaped folding wing. Secondly，based on the Nastran input file for the flutter calculation of the folded wing in the extended state，the parameterized aeroelastic model under any folding angle was reconstructed. Finally，the effects of parameters such as folding angle，flight speed，gust velocity amplitude and hinge damping between different wing segments on the gust response characteristics of the folding wing were examined. The results show that increasing folding angles can effectively alleviate the gust responses， the increase in flight speed and gust velocity amplitude significantly contributes to the responses of wing tip acceleration and wing root bending moment， the presence of hinge-damping parameters can alleviate the acceleration response of the wing tip，but it also increases the bending moment response of the wing root.

To solve the problems of the large amount of assembly information and complex assembly process in modern aircraft component assembly，an online visual assembly model based on digital twin and other related concepts was proposed.Comprehensively considering physical data and process parameters such as the dimensional accuracy of parts，assemblies，and segments involved before and after the assembly process of aircraft components，an information integration platform was built combined with Oracle database technology，which realized the data interaction between physical space and twin space in the form of mapping tables，visually managed various information in the assembly process.The model improves assembly efficiency，at the same time helps staff make scientific decisions.

In the context of the dual-carbon goal，the digital transformation of manufacturing enterprises is of great significance in promoting green transformation and achieving a balance between economic and environmental benefits.A-share listed companies in the manufacturing industry from 2011 to 2021 were taken as the research sample and empirically examined the impact of digital transformation on ESG responsibility performance of manufacturing enterprises.The results show that digital transformation positively promotes ESG responsibility performance of manufacturing enterprises and the conclusions are still valid after robustness test and endogeneity test.The mechanism test shows that digital transformation can promote the ESG responsibility performance of manufacturing enterprises by alleviating their financing constraints.The moderating effect shows that digital inclusive finance can increase the influence of digital transformation on the improvement of ESG responsibility performance of manufacturing enterprises.The heterogeneity analysis show that digital transformation has a more significant effect on ESG performance in manufacturing enterprises with high investments in innovation resources and in low-carbon pilot cities.The study conclusions provide countermeasures and suggestions for enhancing ESG responsibility performance from the government and enterprise levels.

The carbon emissions from the power industry remain a central focus of China’s current carbon reduction efforts.The production-related and consumption-related carbon emissions from electricity across 30 provinces in China were analysed and clarified the scale and pathways of spatial and sectoral transfers of carbon emissions between these provinces.The primary analysis was conducted using the multi-regional input-output （MRIO） model.The results indicate that carbon transfer is the primary driver of regional disparities in carbon emissions related to both production-related and consumption-related.Overall，the trend revealed that carbon emissions are being transferred from economically developed provinces to less developed provinces with surplus electricity supply.At the departmental level，the majority of power transfers are caused by electricity demand from the construction and service industries，which accounts for 57.89% of carbon emissions.It is effectively identified that the spatiotemporal characteristics and transfers of carbon emissions from electricity across various provinces，providing a scientific basis and theoretical foundation for the development of carbon reduction programs for the power industry at the provincial level.These efforts will contribute to achieving the goals of carbon peaking and carbon neutrality.

In response to the complexities of fault signal transmission path，instability and difficulties in extracting fault feature for aircraft engine main bearing，a fault recognition method was proposed based on the fusion of time-domain feature parameters，frequency-domain feature parameters and intrinsic mode function （IMF） energy moment feature parameters for dimensionality reduction.Firstly，60 groups of bearing rolling element fault，inner ring fault，outer ring fault and bearing without fault data were selected respectively then time-domain，frequency-domain and energy moment features were extracted from these instances.Addressing the issue of high dimensionality，extensive data and redundant information of the fusion vector composed of three parameters，principal component analysis （PCA） was employed to reduce the dimensionality of these data and effective principal components were extracted based on cumulative contribution rates of principal components.Finally，the dimensionality reduction feature vectors were input into the support vector machine （SVM） for pattern recognition to diagnose the types of bearing faults.The results demonstrate that compared to models employing single feature parameters，this method effectively extracts fault feature vectors from complex signals.Subsequently，it accurately identifies and classifies fault types using these feature vectors，achieving a fault recognition rate of 98.75%.

In the flight test measurements of civil aircraft noise airworthiness certification，the influence of meteorology is crucial to the final test results.In order to ensure that applicants correctly measure meteorological parameters，correct noise data for meteorological effects and obtain accurate test results，technical index requirements was put forward for test equipment used by ground weather stations，vehicle-mounted weather stations and meteorological aircraft.The review guidance requirements were given for measurement methods，test flight methods，data validity，etc.The atmospheric stratification method and the quadratic interpolation method of the atmospheric sound attenuation coefficient used to correct the noise measurement data were proposed，analyzed，and verified.In the process of noise airworthiness measurement data correction，the noise results can meet the baseline conditions and airworthiness requirements by calculating the atmospheric sound attenuation coefficient and correcting the measurement data.The meteorological data test flight，measurement，analysis，and data correction methods proposed improve the accuracy and reliability of the final noise results and ensure the comparability and reliability of noise test results under different meteorological conditions.

In order to explore the changes in the flow and heat transfer characteristics of the internal cold channel in turbine blades，the effects of rotation numbers 0，0.03，0.06，0.09 and 0.3 on the flow resistance and heat transfer capacity of constricted and expanded serpentine channels with inlet Reynolds number Re=3×10⁴ were investigated.The results show that the Coriolis force and centrifugal force together push the fluid in the center of the channel to the pressure surface，and then the upper wall of the channel is returned to form a vortex with the central fluid，thereby changing the flow structure in the channel.With the increase of the rotation number，the channel resistance coefficient shows a trend of first increasing and then decreasing.When the rotation number rises from 0 to 0.3，the channel resistance coefficient decreases by 53.79%.Rotation destroys the boundary layers of the suction surface and the pressure surface by strengthening the fluid disturbance to enhance the overall heat transfer capacity of the channel.When the rotation number increases from 0 to 0.3，the local heat transfer capacity of the first and second processes increases significantly higher than that of other processes，and increases by 68.49% compared with the relative stationary channel.The overall heat transfer capacity of the constricted and expanded serpentine channel is increased by 21.18% compared with the stationary state.

In order to solve the flight stability issue of small-sized single-person rotorcraft，considering the small-sized and heavy load of the entire aircraft，a stability-enhancing structure scheme was proposed and modal simulation analysis was conducted. Firstly，the center of gravity was determined to ensure that the design met the requirements of flight stability.Secondly， a stability-enhancing structure scheme was proposed to address stability issues such as sway and vibration.Finally，the damper scheme was determined through cockpit dynamics simulation with different damping values of damper and the mode simulation analysis was completed.The results show that the target aircraft can achieve stable flight and meet the practical application requirements through reasonable structural design and flight control strategies while maintaining a small size.

Taking a six degrees of freedom desktop upper limb rehabilitation robot （DULRR） as the research object，it was observed that traditional position control cannot meet the needs of patient rehabilitation training and may lead to secondary injuries during the rehabilitation process.To address this issue，a position closed-loop adaptive compliance control method was proposed.Firstly，based on the kinematic model of DULRR，a position controller based on fuzzy PID was constructed.Then，utilizing the impedance model’s ability to convert force signals into velocity and position signals，an adaptive compliance controller based on pressure sensors was proposed.Combined with the proposed fuzzy PID controller，a complete DULRR passive rehabilitation training control method was formed.Finally，the superiority of the adaptive compliance control method based on position closed-loop was verified through simulation analysis and prototype experiments.The experimental results show that compared with traditional PID controllers，fuzzy PID in the DULRR system has shorter response time and smaller steady-state error，demonstrating better trajectory tracking ability.Meanwhile，the controller exhibits good flexibility，meeting the needs of early passive rehabilitation training for patients and avoiding secondary injuries during the rehabilitation process.

To improve the surface quality of TC4 alloy prepared by selective laser melting，the ultrasonic assisted magnetic abrasive finishing technology for polishing was adopted.Simulated the size and distribution of magnetic flux density under different magnetic pole dimensions and finishing areas.The cavitation bubble in the composite field force was analysed，the magnetic field on the cavitation effect on the influence mechanism was studied.The changes in surface morphology of the specimen before and after applying ultrasonic vibration were analyzed.The influence of amplitude，finishing rotate speed and finishing time on surface roughness were studied.The results show that as the diameter and height of the magnetic poles increase，the magnetic induction intensity increases and the distribution of magnetic flux density gradually changes from uneven to uniform.The finishing effect is better at the position of 80 mm，with an average magnetic field force of 17.12 mN and a surface roughness of 0.673 μm.After applying ultrasonic vibration，the original surface bumps and defects are completely removed，the surface exhibites fine scratches and the surface roughness is reduced to 0.243 μm.Compared with the magnetic abrasive finishing process，the surface roughness is reduced by 60%.The suppression effect of the magnetic field on the cavitation effect reduces the energy released when the cavitation bubble collap-ses， thus reducing the damage caused by the cavitation effect on the surface.