This paper analyses the main research on quadruped robots，based on the motion and stability control requirements of quadrupedal robots， the key technologies of quadrupedal robots， such as mechanism design， kinematics and dynamics analysis， gait and foot trajectory planning， joint actuators， motion stability control， etc.， are sorted out and summarised， and the logical relationship between each technology module is constructed， so as to systematically illustrate the motion and stability control architecture of quadrupedal robots， which can be used as reference for the researchers of foot-type robots.

Image information hiding technology can achieve the goals of confidential communication， copyright authentication and other information security protection behaviors during the transmission of pictures by hiding information covertly in images， which is one of the hotspots of current research in the field of information security. Firstly， we discuss the important and difficult problems of image information hiding methods based on deep learning. Secondly， we discuss the deep learning-based image steganography method from three perspectives： structural features， training features and application features. Then， we introduce the main datasets and evaluation criteria related to the domain and summarize the experimental performance. Next， this paper summarizes the applications of image information hiding techniques. Finally， we discuss the research directions of image information hiding techniques to provide insights and suggestions for the further developments in the field.

In order to improve the efficiency of tire virtual optimizing and shorten the vehicle development cycle， this paper decouples the lateral deformation of the tire camber and sideslip combined situation， and proposes a method of equating the camber as a load function. A dimensionless expression format for pure sideslip is established， and the estimation of tire camber and sideslip combined mechanical characteristics is achieved. The main advantage of this estimation method is that it realizes the dimensionality reduction in the expression of the camber and sideslip combined situation， and uses the input of the pure camber and pure sideslip to obtain the coupled mechanical characteristics of the two. Finally， the dimensionless estimation process is used in the UniTire model to verify its effect.

A modeling method under lateral cyclic loading is proposed for the complex multi-bolt connection loosening problem. Firstly， the thread stress surface is divided into n small sectors， and the self-relaxation mechanical model of multi-bolt connection is established considering the interaction between bolts. Secondly， the self-relaxation model is solved by Matlab software and compared with Nassar model to verify the correctness of the model. Finally， considering the influence of different bolt number， hole clearance and connection spacing， the relaxation characteristics of multi-bolt connection are studied. The results show that during the loosening process， the bolt head undergoes a bending-sliding-bending cycle process， and the bolt tension shows a wave downward trend. When multiple bolts are connected， the number of loosening cycles decreases linearly with the increase of hole clearance. With the equidistant increase of bolt connection spacing， the number of bolt loosening cycles shows a decreasing trend. Compared with the number of bolts， the change of bolt hole clearance and connection spacing is more likely to affect bolt loosening.

Aiming at the linear guide assembly process， due to the preload load caused by the assembly deformation mechanism is not clear resulting in the guide assembly straightness is difficult to predict the problem. In this paper， based on the establishment of linear guide assembly deformation model， the ideal and actual conditions of the guide assembly deformation is discussed. Secondly， considering the influence of manufacturing error of the guide rail mounting surface on the guide rail assembly accuracy， it puts forward the integrated assembly deformation and manufacturing error of the guide rail assembly straightness error prediction model， and puts forward the control method of the guide rail assembly accuracy based on the assembly deformation and the inverse repair manufacturing error. Finally， the experimental bench is built and the accuracy of the theoretical model and accuracy control method is verified through experiments， and the results show that the straightness error of the guideway assembly is reduced by 64.5%.

This work focuses on the hot corrosion performances of CrCoNi medium entropy alloy （MEA） prepared by laser melting deposition （LMD）， and solves the problem that affecting laws of temperature on the hot corrosion kinetics， corrosion morphology and product of MEA. The following results are obtained： the phase of the formed alloy is FCC phase， and the sample contains a small amount of pores and cracks. The microstructure is a mixed dendritic substructure of equiaxed cell and column dendrites. The hot corrosion rates k_p at three temperatures of 700，900 and 1 100 ℃ are 3.920 37×10⁴， 0.002 36， and 0.005 49 mg²/（cm⁴·h）， respectively. The hot corrosion rate increases with the increase of temperature at three different temperatures， and the hot corrosion kinetics curve basically follows the parabolic law； The corrosion layer products are Cr₂O₃ and a small amount of NiCr₂O₄ and CoCr₂O₄ spinel phases. Under the combined action of volatile chlorine gas and thermal stress， the corrosion layer will undergo damage and peeling. The research results have theoretical value for promoting the application of LMD-prepared MEA in high-temperature structural components.

Abaqus software is used to simulate the cracking of the bearing seat of the rear axle reducer housing. The influence of the depth of the laser prefabricated cracking groove on the cracking load and deformation is investigated through J-integral and Z-direction tensile stress at the tip of the cracking groove. The results show that the J-integral of the corresponding node at the middle wall thickness of the bearing seat remains at its maximum during the entire simulated loading process， and cracking occurs at this position. Referring to the cracking of the connecting rod， when the estimated cracking load of the shell bearing seat is 603 kN and the groove depth is greater than 0.7 mm， the simulated maximum tensile stress of the corresponding node at the middle position of the bearing seat wall thickness exceeds the tensile strength， and cracking occurs. When cracking occurs， the bearing seat housing only enters a plastic state in a small local area at the cracking position of the cracking groove； Based on the J-integral criterion， the simulated cracking load for different cracking groove depths is determined， and the cracking groove depth and load curve are plotted and fitted. The corresponding groove depth for 603 kN is 0.797 mm. And a cracking experiment is conducted on a laser prefabricated cracking groove of 1.0mm depth. The actual cracking load and simulated cracking load have an error of 4.30%， and the change in the inner diameter of the specimen is 0.16~0.24 mm， which is less than the allowable plastic deformation （≤ 0.4 mm） for bearing hole cracking processing. Based on the comprehensive simulation analysis results and combined with actual production， the depth parameter range of the cracking groove in the bearing seat of the rear axle reducer housing processed by laser is 0.8 mm to 1.0 mm.

Based on the stress field formula near the crack tip，double-sided V-notch component models with initial cracks were established．The effects of notch depth，crack length，opening angle，material parameters and other factors on the notch stress field were quantified．A simple formula for solving the stress intensity factor SIF of the double-sided V-notch component with initial cracks was derived，which could be used for rapid prediction of the stress field and SIF of the notched component with cracks．The comparison results show that the error between the simplified formula and the theoretical method and finite element simulation results is relatively small， which verifies the effectiveness and accuracy of the improved formula．

In order to explore the influential mechanism of multi-scale ride-hailing travel demand， the travel demand of ride-hailing are analyzed based on the multi-source data. Constructs a multi-scale geographically weighted regression （MGWR） model with short and long distance ride-hailing travel demand as the dependent variable. The effects of built environmental attributes such as road network， land use， population density and public transportation on the demand for ride-hailing and their spatial heterogeneity were revealed. The model results show that the fit of the multi-scale geographical weighted regression model is better than the traditional geographical weighted regression （GWR） model and the ordinary least square （OLS） model， and the influential factors for ride-hailing travel demand have significant spatial heterogeneity. The primary roads density is positively correlated with the short-distance ride-hailing in the city center， and negatively correlated with long-distance ride-hailing in the city periphery. Population density is positively correlated with long-distance ride-hailing in the suburbs， and negatively correlated with the demand for short-distance ride-hailing in the central urban area. Short-distance ride-hailing competes with public transport in the urban centers， while long-distance ride-hailing complements the lack of public transport services around the city. The findings can not only dynamically optimize vehicle configuration and scheduling， but also promote the sustainable development of ride-hailing and shared mobility.

To determine the transport capacity by mountain scenic spots to carry tourists during holidays， an adaptation method of scenic spot transport capacity based on short-term passenger flow forecast was proposed. Based on highway traffic data， the traffic flow was converted into passenger flow， and a CNN-LSTM hybrid model for short-term passenger flow prediction was proposed； Gaussian function was used to fit the discrete data of passenger flow forecast， and breadth-first search algorithm was used to obtain the departure frequency that fitted the passenger flow curve； The paper determined the reasonable constraint conditions of vehicle operation in mountain scenic spots， considering the key parameters such as departure frequency， the number of passengers carrying capacity and traveling tiem of the trip， a capacity adaptation model by deficit function was proposed. Taking Jinsixia scenic area as a case study， the models proposed were verified. The results show that the CNN-LSTM hybrid model can effectively predict the short-term passenger flow in mountain scenic spots， and the R² of the model can reach 0.92 at the time granularity of 15 min. Compared with the traditional "full-passenger-ready-to-go" scheduling mode， the capacity adaptation model reduces the capacity demand from 57 to 28， effectively reducing the fleet supply. The research will be beneficial for short-term prediction of passenger flow in mountain scenic spots and accurate calculation of transportation capacity demand in holidays.

Firstly， moving average method is adopted to decouple the coupled extreme stresses produced by temperature and vehicle loads， the low-frequency data after processed by moving average method is the trend item information， the initial data minus the trend item is the vehicle load effect information， and the trend item minus its mean is the temperature load effect information. Secondly， a bivariate Bayesian dynamic linear trend model （BDLTM） is built to predict and analyze low-frequency extreme stress， GRU neural network model is provided to predict and analyze high-frequency extreme stresses. Finally， the dynamic coupled extreme stresses are predicted. The monitoring coupled data from Tianjin Fumin Bridge is provided to illustrate the feasibility and application of the proposed model. The research results of this paper will provide the theoretical foundation for preventive maintenance and decision-making of the service bridges.

Although machine learning models for predicting concrete creep have been numerous studied， but only a few studies have distinguished concrete strength. Firstly， based on NU-ITI database， three machine learning models BPANN， SVR and XGBoost are used to build a prediction model for concrete creep. The results indicate that XGBoost can effectively predict the creep of concrete （R²=0.972 9）. Secondly， through the analysis of correlations among parameters of high strength concrete， the parameter groups with the highest and lowest correlation coefficients were identified. Based on the parameter selection， the XGBoost models was recalculated for high strength concrete creep， revealing that excluding weakly correlated parameters significantly reduces the robustness of the computational results. This study demonstrates that there are varying degrees of correlation among parameters affecting the creep of high strength concrete. The exclusion of strongly correlated parameters has a minor impact on the accuracy of the model calculations， while the exclusion of weakly correlated parameters has a more significant effect. The research findings can serve as a reference for modeling the creep of high strength concrete.

To investigate the shear properties and stress-strain relationships of subgrade compacted clay， a series of unconsolidated and undrained triaxial tests were carried out on Changsha clay under different degrees of compaction， moisture contents， loading rates and confining pressures. The results show that the elastic modulus and ultimate strength decay with the decrease of degree of compaction， the increase of moisture content and the decrease of confining pressure， but there is a slight fluctuation with the loading rate. The Mohr-Coulomb strength criterion of subgrade clay under complex conditions is established to describe the variation law of the strength of subgrade clay with various factors. The total cohesion and total internal friction angle increase significantly with the increase of degree of compaction and the decrease of moisture content. With the increase of loading rate， the total cohesion decreases first and then increases， and the total internal friction angle increases first and then decrease. However， the fluctuation amplitude of these two indexes is weak due to the change of loading rate. The unified characterization method on stress-strain curves of Changsha clay is proposed， which can reasonably describe the three types deformation curves with strain softening， stability and hardening.

To investigate the feasibility of using magnesium oxychloride cement solidified sludge as subgrade， dynamic triaxial and microstructural tests were conducted on solidified sludge under different wet-dry and freeze-thaw cycles and loading conditions. The influences of confining pressure， dynamic load amplitude and loading frequency on the dynamic characteristics of solidified sludge were studied in terms of dynamic shear modulus and damping ratio， and a prediction model for dynamic shear modulus considering wet-dry and freeze-thaw cycle numbers was established. Quantitative analysis of microscopic pore parameters revealed the microstructural evolution of solidified sludge after different wet-dry and freeze-thaw cycle numbers， and correlation analysis was performed between microstructural parameters and macroscopic mechanical properties. The results show that after wet-dry and freeze-thaw conditioning， the dynamic shear modulus of specimens decreases while the damping ratio increases progressively， and wet-dry cycles lead to greater stiffness deterioration than freeze-thaw cycles. As confining pressure and loading frequency increase， the dynamic shear modulus of solidified sludge increases while the damping ratio decreases. Wet-dry and freeze-thaw cycles increase the porosity and coarse pores of solidified sludge， and the pore shape gradually transforms into smooth lamellar. The cycle number and loading frequency have more significant effects on the dynamic characteristics of sludge than confining pressure and dynamic stress amplitude. Among the microstructural morphology parameters， porosity has the greatest influence on the dynamic properties of sludge.

This article proposes an improved THGS ResNet-18 recognition model for fast and accurate recognition of rice brown spot images. Firstly， apply Tent chaotic mapping to improve the hunger game search （HGS） algorithm， solving the problem of excessive randomness in the population initialization of the HGS algorithm. Secondly， the improved HGS algorithm hyperparameter is applied to optimize ResNet-18 model. Finally， the improved model THGS ResNet-18 was used to recognize 5 064 rice leaf images， and compared with other four ResNet-18 models improved by swarm intelligence algorithm for seven evaluation indicators. Experiments showed that the accuracy rate of the model proposed in this paper increased by 5.22~6.09 percentage points， sensitivity by 3.53~5.31 percentage points， specificity by 7.38 percentage points， precision by 6.95~7.13 percentage points， recall rate by 3.53~5.31 percentage points， f-measure by 5.22~6.20 percentage points， and g-mean by 5.24~6.13 percentage points.

To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles （IoV）， a deep deterministic policy gradient caching （DDPGC） method was proposed. Firstly， a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache. It then broadcasted this information to the neighboring service requesting vehicles （SRV）. SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache， taxi， and cloud server when such requests arise. Secondly， a neural network was deployed in taxis and SRV to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data. Finally， when SRV were located in vehicle sparsity and could not obtain request data from neighboring vehicles， a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy. Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio， reduce the frequency of SRV interaction with the cloud server， and effectively protect user privacy security.

To address the problems of load imbalance， task delay， and increased energy consumption caused by limited device resources and complex task variations in mobile edge computing， a computing task offloading approach with delay and energy constraints for edge-cloud collaboration is proposed， inspired by the cooperative foraging search of sparrow populations. Firstly， adapting to mobile edge cloud collaboration， designing the flyer improved producer update， sine-cosine perturbed follower update， and adaptively adjusted alerter update， a multi-strategy improved sparrow search algorithm （MSSA） is proposed to optimize task offloading location. Then， considering the task maximum completion deadline and delay relaxation variables， incorporating the timeout penalty energy consumption， a heuristic task offloading with MSSA algorithm （HTMA） is proposed， which greedily compares the total task delay and total task energy consumption of pre-offloading location sets under different delay constraints to further optimize task offloading. Experimental results show that compared with similar algorithms， MSSA can improve the optimization accuracy， convergence speed， and robustness of location search. Moreover， HTMA adapts to network changes with better performance of average task completion delay， total task energy consumption， and node load balancing degree.

Aiming at the problem that the existing sentiment classification methods generally did not fully consider the user's personalized characteristics and ignore the influence of time factor on the sentiment classification results， a new method for text sentiment classification based on knowledge distillation and comment time is proposed. Firstly， in order to solve the problem of less labeled data with high quality， the RoFormer-Sim generative model is used to augment the training text data. Then， a sentiment score prediction model of comment text based on multi-feature fusion is proposed by introducing comment time attribute to extract users' personalized information from user's historical comments. Finally， in order to improve the generalization performance for cold start users， the knowledge distillation theory is introduced， and SKEP model is used to enhance the versatility of the sentiment classification model based on multi-feature fusion. The experimental results on the real dataset crawled from the Chinese stock page show that compared with typical methods such as SKEP and ELECTRA， the accuracy of the proposed method is improved by 3.1% and 0.9%， and the F₁ value is increased by 2.7% and 1.0%， respectively， which verifies its effectiveness in improving the performance of sentiment classification.

Confident learning plays an important role in the training of low-quality labeled data of medical images， but the current application of confident learning is based on the mean teacher model， and the possibility on other networks is not discussed. To solve this problem， a segmentation model based on confident learning and collaborative training is proposed in this paper. The model uses two different networks， encourages the output of the two networks to be consistent， and then compares the output of one network with the original low-quality label by using confident learning to modify the low-quality labeled data so as to provide an effective training reference. The proposed model has been compared on three different modal medical image datasets， and the experimental results show that the segmentation effect of the model is better than that of the existing confident learning model.

The traditional self-supervised monocular depth estimation model has limitations in extracting and fusing shallow features， leading to issues such as omission detection of small objects and blurring of object edges. To address these problems， a self-supervised monocular depth estimation model based on improved dense network and wavelet decomposition is proposed in this paper. The whole framework of the model follows the structure of U-net， in which the encoder adopts the improved densenet to improve the ability of feature extraction and fusion. A detail enhancement module is introduced in the skipping connections to further refine and integrate the multi-scale features generated by the encoder. The decoder incorporates wavelet decomposition， enabling better focus on high-frequency information during decoding to achieve precise edge refinement. Experimental results demonstrate that our method exhibits stronger capability in capturing depth estimation for small objects， resulting in clearer and more accurate edges in the generated depth map.

Aiming at the problem that the existing video traffic parameter extraction method relies too much on manual labeling and the single perspective cannot effectively correct the dynamic driving deviation of on-site vehicles， a multi-view video traffic parameter extraction method that can split the lane demarcation line is proposed. This method consists of an automatic generation module for labeling points and a module for multi-view correction. The automatic generation of label points module realizes the process of automatically generating label points by constructing a reference block based on the dividing line of equal-length lanes. The multi-view deviation correction module proposes a variety of mapping methods for the boundary between vehicles and lanes and a correction speed measurement method based on the average speed probability density function to correct two types of deviations generated by vehicles when driving dynamically. The experimental results on the public dataset and the measured dataset show that the speed extraction accuracy of the proposed method is better than that of other speed measurement methods， and has certain universality.

The implementation of autonomous driving technology required high-precision recognition of traffic signs. However， due to their high similarity， small size， and vulnerability to outdoor environmental factors， achieving real-time and accurate detection posed significant challenges. In response to the limitations of traditional neural network design approaches， an algorithm based on self-selecting architecture was proposed to automatically adjust the network structure， thereby enhancing model performance and efficiency. The algorithm adopted a two-stage training approach to optimize the selection of network paths. Moreover， gradient propagation was employed to train the hyperparameters for multiple loss functions， replacing the conventional manual tuning with a dynamic loss network scheme. The results demonstrated that the proposed algorithm achieved an accuracy rate of 95.74% and a detection speed of 146.58 frames per second on the GTSRB dataset， while maintaining a model parameter size of only 0.46Mb， enabling deployment on mobile devices. Compared to the traditional manual design of static networks， the adoption of the self-learning architecture module not only reduced experimental costs but also improved accuracy and performance. Furthermore， it enabled superior detection outcomes in various environments and exhibited a noticeable enhancement in loss convergence speed.

Aiming at the problem of large size differences in infrared small targets and poor detection results in complex scenes， an infrared small target detection method based on cascaded nested U-Net is proposed. First， in order to solve the problem of large size differences between small targets in different scenarios， three depths of U-Net networks were built， and the three U-Net networks were cascaded and nested to form a detection model； secondly， contrast was used information extraction module to further enrich feature information and suppress the interference of dense background noise； finally， the proposed algorithm is compared with five mainstream algorithms. The experimental results show that the performance of this algorithm is better than other algorithms， and the average intersection and union ratio， the precision rate and recall rate reached 78.61%， 93.36% and 81.78% respectively.

Nonlinear noise generally has high uncertainty， difficult to accurately describe spectral characteristics， large steady-state errors， and poor transient performance. Effective control of nonlinear noise is a challenging task. Therefore， a nonlinear active noise control algorithm based on weight optimization AF is proposed. This algorithm uses the sine and cosine components of the reference signal for direct frequency analysis， analyzes the primary noise and narrowband frequency range， obtains accurate noise estimation results， and improves the accuracy and effectiveness of noise control. By training the results of nonlinear active noise estimation and adjusting the weights of the adaptive filter， the reconstruction of the expected output signal is achieved， effectively suppressing the influence of nonlinear noise and improving the control effect. The experimental results show that under this algorithm， the mean square error of signal noise is low and the steady-state error is small， which can adapt to various complex noise environments and effectively improve the quality of noise control， it has high practicality and reliability.

In order to improve the accuracy of facial fine feature recognition， a facial fine feature recognition algorithm considering the correlation between learning interests and micro expressions is proposed. Selecting image entropy as the objective function for facial image segmentation， using particle swarm optimization （PSO） algorithm to optimize the parameters of pulse coupled neural network （PCNN）， determining the optimal values of key parameters， achieving facial image segmentation， and identifying key areas such as eyes and mouth. On the basis of analyzing the correlation between learning interests and micro expressions， the Harris algorithm is used to filter the scale invariant feature transform （SIFT） feature points， accurately locking the key interest points in facial expression images. A strategy based on the maximum coverage area and its adjacent range of feature points to capture the features of each region. The filtered area is used as input for local binary patterns （LBP） feature extraction， and facial fine feature recognition is achieved through support vector machine （SVM） multi classification technology. The experimental results show that the proposed algorithm performs well in facial image segmentation and has high accuracy in recognizing subtle facial features.

At the same time， the inconsistency of spatial coordinate systems further exacerbates the complexity of recognition problems， making traditional recognition methods face problems such as inaccurate feature extraction， low matching efficiency， and low recognition rate. Therefore， a 3D laser point cloud recognition method based on point-to-point feature algorithm and SVD decomposition is proposed. Firstly， the ISS key feature points are extracted from the point cloud data， and through the SVD decomposition algorithm， the identified ISS key feature point cloud and database reference point cloud are adjusted to a unified spatial coordinate system. By combining the FPFH descriptor with the SPFH graph， the spatial geometric characteristics of key point cloud feature points are described， and the point cloud data in the reference point cloud that meets the conditions of closest spatial proximity and the most similar FPFH descriptor is divided into a pair of feature point pairs. Introducing spherical harmonic function and calculating the similarity of point to point features to achieve the recognition of three-dimensional point clouds. The experiment shows that the proposed method can simplify complex point cloud data while preserving local ISS key feature points of the point cloud. Through feature point pair similarity analysis， effective recognition of 3D point cloud data can be achieved.

The spectral resolution of hyperspectral images is very high， and there are many bands of ground objects， so the spectral difference between the target and the background is very small， which is easy to cause spectral confusion， and the accuracy of target detection is low. Therefore， an image object detection method based on improved YOLOv5s algorithm is proposed. A feature pyramid is established and multi-scale weighting is implemented. The weights between different layers in the feature pyramid are used to weight and fuse the features and introduce them into the attention mechanism. The spectral features of the spatial attention mechanism are output， and the feature value is used as a comparison reference. The hyperspectral image features obtained after calibration are used as the input of the improved YOLOv5s algorithm to effectively distinguish the tiny spectral feature differences in the image， avoid spectral confusion， calculate the overlap area between the detection frame and the real frame according to the central value， complete the target detection， and ensure the detection accuracy. Experiments show that the proposed method has a high accuracy for detecting ground objects in hyperspectral remote sensing images. When detecting 1 057 p pixel images， the frame rate is as high as 60fps， and the comprehensive performance is excellent.

Therefore， with the human pose recognition problem of robots as the core， a local feature recognition algorithm for human pose based on an improved RBF neural network is proposed to improve recognition accuracy. Using a depth camera to obtain three-dimensional orientation data of human joint points， normalizing the orientation data， and constructing three-dimensional coordinates of joint points； Considering the differences between different individuals， in order to achieve nonlinear mapping and optimization of human pose data， accurately identify different individual poses， a Newrbe function is used to construct an RBF neural network， extract feature vectors of human pose data， and provide important basis for recognition； To enhance the ability of RBF neural networks to handle different individual pose differences， ensure recognition accuracy and adaptability， particle swarm optimization algorithm is used to improve the neural network， and genetic operations are performed on particles with specific probabilities to achieve network optimization and obtain local feature recognition results of human pose. The experimental results show that the proposed algorithms have relatively low relative errors， can be maintained below 0.8， high recognition accuracy， and the loss function is minimized when the number of iterations reaches 20. The convergence speed is fast， which can provide a solid foundation for human-machine interaction in the field of agricultural mechanization.

When using near-infrared spectroscopy and machine learning methods to quickly identify the quality of Pu-er tea， the spectra collected by medium and low-end near-infrared spectroscopy acquisition equipment have the characteristics of high dimension， overlap and large noise， which seriously affects the accuracy of modeling. This paper proposes a noise-robust feature extraction method， which is combined with support vector machine （SVM） classifier to establish the quality identification method of Pu-er tea. Firstly， the noise-robust feature extraction method， principal component analysis （PCA） and successive projections algorithm （SPA） are used to extract the features from the obtained near-infrared spectral data. Then， SVM is used to train the data after feature extraction to obtain the identification model. The comparison of the identification results of the model shows that for the noiseresidual near-infrared spectral data， the noise robust feature extraction method in this paper can effectively resist the influence of noise and propose feature variables from the high-dimensional spectrum to improve the accuracy of the identification model. The accuracy， recall， specificity， accuracy and F-score predicted by the identification model were significantly higher than those obtained by the other two methods. For the detection of ancient Pu-er tea and non-ancient Pu-er tea， the accuracy and recall predicted by the identification model in this paper have reached 92.06% and 95.38% respectively， indicating that the identification model has good identification ability. The research results provide theoretical reference and basis for accurately judging the quality of Pu-er tea in practical application.

To address the issues of strong subjectivity and low accuracy in traditional methods for predicting innovative talents in higher education， this paper proposes an intelligent predictive model that combines a Particle Swarm Optimization algorithm， enhanced with an Information-Guided Communication Search strategy， with a Kernel Extreme Learning Machine. This model aims to more scientifically and objectively identify and select innovative talents by utilizing the improved Particle Swarm Optimization algorithm to enhance population diversity and global search capabilities， thereby improving the classification performance of the Kernel Extreme Learning Machine. To validate its effectiveness， experiments were conducted on a university innovation talent dataset using 10-fold cross-validation. The results demonstrate that the proposed model outperforms several comparative models in key evaluation metrics， including classification accuracy （86.05%）， sensitivity （89.74%）， specificity （83.24%）， and Matthews correlation coefficient （72.42%）. These findings confirm the significant advantages of the proposed model in predicting innovative university talents， offering a new technical approach for the scientific selection and cultivation of talent with promising application prospects.

To address the challenge of estimation accuracy degradation resulting from off-grid caused by compressive sensing-like direction of arrival （DOA） estimation algorithm in polarization-sensitive arrays， this paper presents a joint estimation algorithm for two-dimensional DOA and polarization parameters utilizing the theory of atomic norm minimization （ANM） applied to a single dipole array. Firstly， the proposed algorithm constructs receiving models for different polarization directions using the orthogonal polarization sensitive array's characteristics， which can accommodate the influence of polarization parameters and adhere to the ANM model. Secondly， the algorithm solves a positive semi-definite programming problem to obtain a positive semi-definite Toeplitz matrix， from which DOA information is recovered using the matrix-pencil algorithm. Lastly， the polarization parameters are retrieved using the DOA information and the generalized eigenvalue theory. The effectiveness and superiority of the proposed algorithm are demonstrated through simulation experiments.

Aiming at the limitations of existed electromagnetic compatibility （EMC） evaluation methods or models for wireless communication systems and the actual needs of vehicular communication systems， a five-level novel evaluation model including working environment， signal spectrum， receiver sensitivity， antenna isolation and communication performance is constructed considering the completeness and accuracy of EMC evaluation for vehicular communication systems. The performance of the constructed model is validated using the vehicular communication system of an armored vehicle as an example. The model can evaluate whether there is interference between the working environment and the signal spectrum of the vehicle-mounted radio station. The error in the reduction of the receiver sensitivity between calculation and measurement is 5.8%. The calculated isolation of the vehicle-mounted antenna is in good agreement with the measurements. The modulation mode and coding mode of the vehicle-mounted digital communication system with better performance are simulated and analyzed. When the receiver sensitivity is reduced by 6 dB， the vehicle communication distance is reduced by 50%. The simulation and measurement results show that the proposed model is suitable for the evaluation of EMC of vehicular communication systems for armored vehicles.

Firstly，a key negotiation based authentication and security trust scheme is proposed for security and privacy issues in vehicular ad-hoc network （VANET）. Secondly， an elliptic curve cryptosystem based signature generation is used， and a low-consumption key distribution scheme is proposed， where the communicating parties exchange parameters to mutually authenticate and securely generate session keys. Finally， the communicating entities are authenticated by three-way two-way authentication. After security and performance analysis， it shows that the scheme can improve the efficiency of identity authentication and reduce the system overhead， which has good theoretical and application value， and the system is deployed in conjunction with the Network Security Level Protection level 3 requirements standard， which can solve the identity privacy protection and security trust problems in the in-vehicle self-organizing network.

To address the issues of slow convergence and limited applicability of reinforcement learning models in automatic driving tasks， a two-tiered reinforcement learning framework is proposed as a substitute for the decision and control layers. Within this framework， the decision layer categorizes driving behaviors into lane keeping， left lane change， and right lane change. Subsequently， after the decision layer selects the appropriate behavior， execution is achieved by modifying the input to the control layer. Then， in combination with reinforcement learning and online experts， a new method RL_COE is proposed to train the control layer. Finally， the proposed algorithm is verified in the highway simulation environment based on Carla and compared with the baseline reinforcement learning algorithm. The results show that this method significantly improves the convergence and stability of the algorithm， and can better perform the driving task.

A hydraulic system was designed for the new dual-clutch full-powershift transmission of the newly developed 223.7 kW （300 hp） heavy-duty tractor， in order to achieve the purpose of uninterrupted shift and transmission cooling and lubrication. The AMESim dynamic shift simulation model was established， the flow and oil pressure properties of the hydraulic system as well as the dynamics of the clutches and synchronizers have been analyzed. The simulation results show that the pressure building time of the clutch hydraulic cylinder is less than 1 s， the pressure relief time is less than 0.6 s， and the pressure is stable during the pressure building/relief. The synchronizer hydraulic cylinder establishes stable oil pressure within 0.3 s； The cooling and lubrication subsystem takes 0.5 s to establish a stable oil pressure of 0.22 MPa， and the maximum flow rate is 57.6 L/min. The clutch completes the smooth transition of power during the shift， and the synchronizer completes the pre-selection before the shift. A new dual-clutch full-powershift transmission test bench was set up for further verification， the oil pressure test results of the clutch and synchronizer hydraulic cylinders are basically consistent with the simulation results， the output speed of the transmission increases from 270 r/min to 310 r/min， and the maximum wave momentum is 31 r/min， the output torque of the transmission is stable around 2 910 N·m， the maximum wave momentum is 10.1% of the stable value， and there is no power interruption during the shifting process. The simulation and test results verify that the hydraulic system can meet the working requirements of the new dual-clutch full-powershift transmission of heavy-duty tractor. The work in this paper can provide reference and guidance for the design and calculation of the hydraulic system of the dual-clutch full-powershift transmission of heavy-duty tractor.