To clarify the research progress of strengthening technology of recycled concrete aggregate， the strengthening methods of recycled concrete aggregate and its improvement effect were reviewed systematically. The effect of different strengthening treatments on the performance of recycled aggregate concrete was evaluated. The problems existing in the application of strengthening methods were summarized， and constructive suggestions were put forward to provide a scientific basis for researchers to design and develop green high-performance recycled aggregate concrete.

To address the issues of multi-scale object segmentation errors， poor correlation between multi-scale feature maps and feature maps at different stages in the DeepLabv3+ network， the following modules are proposed to incorporate，including a global context attention module， a cascade adaptive Scale awareness module， and an attention optimized fusion module. The global context attention module is embedded in the initial stage of the backbone network for feature extraction， allowing it to capture rich contextual information. The cascade adaptive scale awareness module models the dependencies between multi-scale features， enabling a stronger focus on the features relevant to the target. The attention optimized fusion module merges multiple layers of features through multiple pathways to enhance pixel continuity during decoding. The improved network is validated on the CityScapes dataset and PASCAL VOC2012 augmented dataset， and the experimental results demonstrate its ability to overcome the limitations of DeepLabv3+. Furthermore， the mean intersection over union reaches 76.2% and 78.7% respectively.

Aiming at the challenges of unnecessary leap and significant undulations terrains with large steering angles in path planning of multimodal quadruped robots by Rapidly Exploring Random Tree algorithm， a path planning algorithm solution based on discrete sampling is proposed. The path is preprocessed to remove unnecessary leap and a solution set is obtained by discrete sampling and dynamic programming method. B-spline curves are used to define spline segments and quadratic programming method is used to optimize the final path. The simulation results show that paths planned by the proposed method exhibit an average reduction of 31.4% in the adjustment of robot's center of mass height， a 13.4% decrease in undulation of terrain， an 11.4% reduction in terrain slope angle and a 62.7% reduction in steering angle， which affirm the effectiveness of the proposed method.

In order to improve the poor adaptability of the traditional equivalent consumption minimization strategy （ECMS）， and to further enhance the fuel economy of hybrid energy storage systems， an adaptive energy management strategy considering the prediction of the state of charge （SoC） of Li-ion battery is proposed. Firstly， based on the domestic tram lines and traveling data， a Markov chain is used to construct the typical driving conditions of streetcars under semi-independent right-of-way. Secondly， the SoC of lithium battery is predicted by adaptive Kalman filtering method， the charging and discharging process of lithium battery is optimized， the reliability of lithium battery is enhanced， and the minimum equivalent energy consumption of hybrid energy storage system is taken as the optimization target， meanwhile， the equivalent factor of traditional ECMS is optimized by combining with particle swarm algorithm， so as to realize the reasonable and effective distribution of load power between fuel cells and lithium batteries. Finally， a comparative analysis is carried out in the typical working conditions of the constructed tram under semi-independent right-of-way. The results show that， compared with the fixed-threshold strategy， the proposed strategy reduces hydrogen consumption by 0.63 kg and fuel cell peak current by 57.2 A. Compared with the state machine strategy， the proposed strategy reduces hydrogen consumption by 1.21 kg and fuel cell peak current by 24.6 A， and the fluctuation ranges of bus voltage and Li-ion battery SoC are both improved.

In order to solve the problem of low accuracy of accidents severity prediction caused by data imbalance， a traffic accidents severity prediction model based on deep learning technology was proposed. Machine learning algorithm was used to determine the key variables affecting the severity of accidents， Numerical accidents variables were converted into image data and applied to MobileViT network that combined the convolutional neural network and the self-attention mechanism. Focal loss function was used to adaptively adjust the loss contribution of injury and severe accidents with small data volume， so that the model paid more attention to unbalanced data， and the prediction performance of the model was evaluated by precision， recall and F₁ score. The results show that the overall predictive performance index of the proposed model is above 0.81， which is better than other baseline models， and recall and F1 score are at least increased by 4% and 2.5%. Compared with WGAN-GP-XGBoost and ResNet18 models， MobileViT model has improved recall and F1 score of injury accidents by 25.9% and 4.5% respectively. Compared with the other two models， the prediction performance for severe accidents is the best， with an increase of 8.9%， 4.2%， and 6.7% in precision， recall rate， and F1 score. Compared to other data balancing methods， the MobileViT model enhanced with focal loss as the loss function demonstrates superior performance in imbalanced data prediction.

To investigate the application effect of prestressed steel plate strip reinforcement technology in improving the load-bearing capacity of walls and reducing the degree of deformation， the deformation characteristics of prestressed steel plate strip brick masonry composite walls under applied loads were investigated by considering the failure state of supports.In the experiment， 7 specimens were carefully designed and reinforced with prestressed steel plates， each with different reinforcement situations. In the production of specimens， after pouring the bottom beam， a brick masonry wall is made. After pouring the top beam， the surface is smoothed to form an overall structure with the bottom beam and brick masonry wall. Transport the cured specimens to the laboratory for reinforcement with prestressed steel plates. The cutting length of the longitudinal steel plate strip should be consistent with the height of the wall. If vertical preloading stress is required， the cutting length will correspondingly increase by 2 or 4 mm. The horizontal steel plate strip matches the actual width of the wall. Install testing instruments and strain gauges， start loading until the support function of the composite wall fails， and stop the test loading.The results demonstrate a positive correlation between the reinforcement performance of prestressed steel plate strips on walls and the load-bearing capacity of the walls. Before the wall support fails， the curve shows a decreasing trend， followed by the failure of the wall support. A prestressed steel plate strip with strong reinforcement performance can reduce the strain value of the wall itself， that is， reduce the degree of deformation of the wall itself. The application of prestressed steel plate strips can significantly enhance the overall stiffness and stability of walls.

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.

Unequal allocation of container resources in a cluster environment is currently a pressing issue. In response to container load prediction and resource allocation strategies， this paper proposes an empirical mode decomposition-long short-term memory （EMD-LSTM）-based algorithm for predicting container resource load groups in the preprocessing of astronomical data. An adaptive recommendation value generation algorithm based on load prediction information is introduced， which automatically allocates container computing resources according to the degree of load fluctuation. The accuracy of load prediction was verified using simulated data and real astronomical observation data. Experimental results demonstrate that the proposed algorithm outperforms the triple exponential smoothing method and a single LSTM network model in terms of prediction accuracy. In real-time preprocessing of astronomical data， compared to the default strategy， the recommended value generation algorithm proposed in this paper effectively improves the utilization efficiency of computing resources.

A novel in-vehicle network intrusion detection method based on controllerarea network （CAN） bus data was proposed and named as IncepNet method. First， a real Car-hacking dataset was selected for data preprocessing. Image processing methods were used to reorganize the time-series data in this dataset according to certain rules， and converted it into image data suitable for use as the input of convolutional neural network. Next， the existing residual network （Inception-ResNet） was optimized and followed by the addition of a long short-term memory （LSTM） layer with multi-batch normalization and a Dropout layer. Finally， a confusion matrix including recognition rate， accuracy， F₁-score （F₁-score）， and number of false alarms （FAR） was used to demonstrate the superior accuracy and reliability of the model. The results show that the model has low false alarm rate， high detection accuracy and high detection rate， and its efficiency is significantly better than previous detection methods based on other machine learning.

An improved Faster-RCNN algorithm for detecting small traffic signs was proposed， which addresses the issues of poor recognition performance of distant small targets and high computation cost in real-world traffic scenes affected by weather and lighting conditions. Based on the basic architecture of Faster-RCNN， the algorithm reconstructs the backbone network and improves the region proposal network to make the network framework lightweight. A multi-scale feature fusion network is designed by integrating the scSE attention and GSConv modules， and the Anchors box size was updated to improve the localization and recognition of traffic sign targets. The ROI Align pooling operation with bilinear interpolation for each target subregion was used to preserve the detailed features of the target region and improve the ability to capture details of distant targets. The balanced L₁ loss function was adopted to address the problem of imbalance between samples with large gradient difficulty and those with small gradient easiness， thus improving the training effect. Experiments were conducted on the expanded TT100K dataset. Results show that compared with traditional Faster-RCNN， the model weight is reduced by 200 MB， and detection accuracy is improved by 21.3%. The algorithm achieves a detection accuracy of 85% in low-intensity environments such as cloudy days， which helps improve the traffic sign detection performance in extreme environments.

This article initially provides an overview of the processes and standard regulations involved in the safety verification and validation of autonomous vehicles. Building upon the human-vehicle-road system theory， the article further introduces a novel classification approach， categorizing and summarizing the current technologies and assessment standards for safety verification and validation in autonomous vehicles. It also consolidates and comparatively analyzes three major categories of methods： those based on real-world scenarios， virtual scenarios， and a combination of both. The article conducts a comparative evaluation of the limitations， advantages， and disadvantages of 16 different verification and validation methods across eight characteristic dimensions. Finally， it briefly extrapolates on the challenges and future prospects in the research of safety verification and validation schemes for autonomous vehicles.

To address the problems of low programming efficiency， high development difficulty and high Vivado HLS resource utilization in the hardware development process， a pipelined direct parallel finite impulse response filter was designed and implemented with a 127-order Hamming window using Vivado and Vitis HLS platforms. And the filtering performance of HDL FIR IP， HLS FIR IP， and XILINX FIR IP in the Vivado Simulator environment was compared. Detailed analysis was conducted on the differences in resource utilization， timing， power consumption and execution time among different implementation methods. The experimental results show that under the same conditions， the resource utilization of HLS FIR IP is reduced by 1% compared with HDL FIR IP and XILINX FIR IP， the execution time is reduced by 24.5% and 808.2% respectively， and the code size is saved by 98.5%. Based on the proposed experimental method， a comparison was made with previous work to objectively analyze the efficiency differences of different development platforms and methods under certain conditions. The results show that our design method can significantly reduce the usage of logic units and storage resources and improve the development efficiency.

A low illumination image enhancement algorithm based on dual branch channel priors and Retinex is proposed to address the issues of local dimming， detail loss， and over enhancement in existing algorithms. Firstly， on the basis of Retinex， a dual branch bright dark prior feature guidance method is proposed， and a bright channel prior feature module and a dark channel prior feature module are designed to guide the network to suppress reflection component noise and improve lighting component brightness； Secondly， a pixel mixed attention mechanism module is designed to learn targeted features from three dimensions： channel， space， and pixel； Thirdly， a dark channel refractive index estimation module is designed to amplify image detail features. Finally， a mixed loss function is used to adjust brightness， contrast， noise， and color constraints. The experimental results on public datasets show that compared with 10 advanced algorithms， this algorithm improves image brightness while reducing color distortion and detail loss， achieving the best visual effects and quality indicators.

This article reviews the parameters that influence free abrasive processing procedures and the models used to predict material removal. The text begins by introducing the common methods and principles of free abrasive processing. It then analyzes the impact of machining parameters on machining efficiency and surface quality. Finally， it summarizes the modeling method for material removal and provides prospects for future research directions.

Confidential computing is an effective method for addressing data security and privacy issues. However， with the increasing diversification and heterogeneity of computing devices， the implementation of traditional confidential computing on diverse heterogeneous devices becomes extremely complex and challenging. How to achieve heterogeneous confidential computing has become a current research hotspot. Based on the implementation technology of confidential computing on heterogeneous devices， a comprehensive analysis and evaluation of heterogeneous confidential computing were conducted. Firstly， the concept and challenges of heterogeneous confidential computing are introduced. Secondly， the software boundary expansion techniques and hybrid boundary expansion techniques in mainstream heterogeneous confidential computing technologies are discussed. The article also summarizes the evaluation analysis methods and standards in current heterogeneous confidential computing research. Finally， we points out the future research directions in the field of heterogeneous confidential computing.

A research on urban passenger transportation planning algorithm based on location potential energy and multi-source data was proposed to address the problems of traffic congestion and resource waste caused by unreasonable layout and inappropriate scale of passenger transportation in some cities. Firstly， an appropriate method was selected to integrate urban passenger transportation multi-source data from the fusion of the original data level， feature data level， and transportation theory level. Then， based on the probability model of traffic distribution and the Furness model， a traffic distribution prediction model was constructed， introducing location potential energy to predict the distribution of passenger transportation trips between traffic analysis communities. Finally， a dual level planning model for urban passenger transportation was constructed， and an improved quantum particle swarm optimization algorithm was used to solve it， in order to achieve urban passenger transportation planning. The experimental results show that the proposed method has higher scores than the comparison method in various evaluation indicators， with the highest score reaching 50.492. The highest congestion rate of vehicles is only 29%， which is practical.

AV1 compression technology increases encoding complexity. In order to meet the requirements of real-time communication （RTC） applications， encoders need to run at low latency， low bit rate， and low encoding time on low-end platforms， which makes using AV1 encoders challenging in RTC tasks. In this regard， this article proposes an AV1 fast intra frame encoding algorithm. By pre calculating the directional characteristics of blocks， during the intra frame encoding direction prediction traversal， significantly different directions from the pre calculated direction are skipped， thereby improving the encoding speed of the libaom-AV1 encoder， saving intra frame encoding time， and accelerating the progress of AV1 in low latency video encoding applications. The experimental results show that the algorithm can save 25.89% of running time， and after saving encoding time， the loss of encoding performance is relatively small.

Aiming at the issues of severe missed detections and low detection accuracy for small targets in the perspective of drone aerial photography， an improved YOLOX network is proposed for the detection of drone aerial images. To enhance the feature learning ability of the network， the ASFF module is introduced in the feature fusion part， and the CA mechanism is embedded in the neck of the network. To enhance the network's learning of positive samples， the binary cross-entropy loss function is replaced with the varifocal loss function. Experimental results show that the improved YOLOX network has better detection efficiency， and its mAP_@50 reaches 91.50% and mAP_{@50_95} reached 79.65%. The visualization results in various traffic scenarios show that compared with other algorithms， the optimized network has a lower missed detection rate and higher detection accuracy， which can be competent for the detection task of small target vehicles， and can provide a reference for vehicle multi-target tracking applications from a high-altitude perspective.

In order to study the energy evolution law and failure behavior of high-strength concrete under conventional triaxial compression state， five groups of tests of C60 and C70 high-strength concrete under different confining pressures were carried out. Based on the test results and the principle of thermodynamic energy conservation， the variation laws of input energy density， elastic strain energy density and dissipation energy density of high-strength concrete with axial strain and confining pressure were obtained. According to the linear growth relationship between elastic strain energy density corresponding to peak stress of high-strength concrete and confining pressure， the failure criterion of high-strength concrete was established. The results have shown that：① The input energy density and dissipated energy density of high-strength concrete increased with the axial strain， while the elastic strain energy density increased with the axial strain in the pre peak stage and decreased after the peak；② The input energy density and dissipated energy density corresponding to the peak stress of high-strength concrete sample increased with confining pressure， and the input energy density and dissipated energy density corresponding to the peak stress of C70 high-strength concrete were greater than that of C60 high-strength concrete under the same confining pressure；③ The failure criterion of high strength concrete based on elastic strain energy density has high accuracy， few parameters and clear physical meaning. The form of the criterion was similar to Hoek-Brown failure criterion， but it has wider applicability；④ The failure criterion of high-strength concrete was a symmetrical hexagon with equal edges and unequal angles in the π plane. The singular points of the failure curve were rounded according to the "turning angle into arc" method. The research results provide a new idea for studying the deformation and failure behavior of concrete materials from the perspective of energy.

Inspired by the microscopic Bouligand structure of mantis shrimp， this paper used four cells （hexagonal， circular， quadrilateral and negative Poisson's ratio） as interlayer basic structures， and established four bionic helical structures and four contrast structures. Using the thermoplastic polyurethane material as the base material， the above structures were fabricated by 3D printing technology. Based on the quasi-static compression tests and numerical simulations， the energy absorption performance of the above eight structures was investigated. The results showed that the energy absorption characteristics of the four bionic helical structures were better， and the specific energy absorption of the bionic hexagon helical structure could reach 531.65 mJ/g. Compared with the corresponding contrast structure， the specific energy absorption of the four bionic helical structures increased by 64.72%， 32.39%， 55.84% and 25.14% respectively， which indicated that the helical stacking distribution between layers effectively increased the energy absorption characteristics of the structures. The deformation and failure of the structures would absorb more energy.

For the cross-domain authentication problem of mobile users in mobile edge computing， we proposed a dynamic authentication framework consisting of three secure authentication models， which provides security and efficient authentication. Built on the authentication framework， a lightweight authentication protocol based on elliptic curve encryption， an edge-centric security authentication protocol， is designed. The protocol ensures the anonymity and untraceability of mobile users and their mutual authentication with edge servers. Security analysis and performance evaluation show that the proposed protocol has better performance advantages in both computational and communication cost and is more suitable for mobile scenarios.

In response to the security issues in industrial networks， a new intrusion detection method is proposed. The specific innovations of the method are divided into two aspects. First， in the process of processing， in order to solve the problem of high dimensionality of the original data， a particle swarm optimize genetic algorithm （PSO-GA） hybrid algorithm with dynamically adjusted parameters was proposed for feature extraction. It successfully screened out a subset of features that are meaningful to model training and accelerated training speed. Secondly， when building a machine learning model， theStacking integrated learning framework is used to generalize the output results of multiple models to improve the overall prediction accuracy. The experimental results on both two datasets show that the detection precision on the publicly available intrusion detection dataset CICDS-2017 has reached 95%， and it also has a 93% precision on a real industrial dataset developed by Lan Turnipseed from the gas pipeline control system.

To predict the possibility of traffic accidents in advance， a traffic accident risk anticipation model gated recurrent unit spatial-temporal transformer（GST） was established based on the combination of vision transformer（ViT）， gated recurrent unit （GRU）， and MLP-Mixer. By modeling spatial-temporal relational learning through ViT， the frame features of predicted targets were enhanced to improve their distinguishability. On this basis， GRU was used to extract temporal relational， and then GRU and MLP-Mixer were combined to enhance the hidden layer frame features， establishing and optimizing spatiotemporal relational， the confidence score of traffic accidents for each time step was predicted based on the corresponding feature frames to predict the probability of future accidents and effectively distinguish between dangerous driving and accident driving behavior. Finally， the proposed model was validated on the public datasets DAD and A3D， and the results showed that the recognition accuracy of the proposed model was superior to other advanced algorithms. The AP on the two datasets reached 59.9% and 94.6%， respectively， demonstrating good predictive performance and generalization ability. In the DAD dataset， the algorithm proposed was compared to the DSTA model. With similar AP， the proposed algorithm can advance the prediction time of accidents by 2.38 seconds， an increase of about 13%. This indicates a significant advantage and provides assistance for road hazard warnings and safe driving.

To measure the carbon emissions at each stage of buses throughout life cycle， this paper divided the life cycle of buses into process cycle and energy cycle. Considering the production， assembly， transportation， and recycling stages of each bus system， carried out research to obtain bus production data， combined the data provided by the company and GREET internal data， used Gabi to measure CO₂ emissions of process cycle. Constructed a CO₂ emissions measurement model that included the stages of energy extraction， production processing， transportation， and usage to estimate the energy cycle CO₂ emissions of electric buses and diesel buses. The result shows that the CO₂ emissions of electric buses are 39.2% higher than those of diesel buses during the process cycle. In the energy cycle， the CO₂ emissions of electric buses are 14.2% lower than those of diesel buses. In a comprehensive life-cycle comparison， the CO₂ emissions of electric buses are 9.73% lower than those of diesel buses.

Addressing the challenges inherent in passive unmanned search and rescue missions at sea， including difficulties in target identification， broad search areas， and slow route planning， a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning， an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the whale optimization algorithm. The validity of the model is confirmed through designated scenario experiments， indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs.

Aiming at the complex semantic rules and potential topic structures in different fields， which result in poor scalability and portability of topic sentence generation， low similarity between Chinese text topic sentences， and high redundancy in topic sentence extraction from Chinese text， a Chinese text topic sentence automatic extraction method based on TextRank algorithm and similarity was proposed. Using the Bi LSTM model for Chinese text segmentation， continuous Chinese text is segmented into independent words. Through mutual information method， Chinese text feature selection is carried out， and feature values are calculated to extract the most relevant and representative features of the task （such as keywords and clue words）. Keywords and clue words are used as important clues and basis for topic sentence extraction. Based on the TextRank algorithm and similarity， various weights and weight coefficients were considered to automatically extract Chinese text topic sentences. The experimental results show that the proposed method has low redundancy in automatic extraction of Chinese text topic sentences， and the completeness， scalability， and portability of the document are all in a good state. Moreover， the results of ROUGE-1， ROUGE-2， and ROUGE-L are all high. Ensure the automatic extraction effect of Chinese text topic sentences， with a high degree of application.

Aiming at the limitations of single-dimensional car-following model to describe vehicle car-following behavior in local multi-vehicle environment， the mechanism of vehicles in adjacent lane influencing the subject vehicle car-following behavior is explored， and a vehicle car-following model more suitable for local multi-vehicle environment is attempted to be established. The driving behavior variable reflecting the influence of vehicles in adjacent lane was determined by correlation analysis. Vortisch indicator of similarity（VIS） was used to characterize the influence of vehicles in adjacent lane on car-following behavior. Chi-square independent test and kernel density curve were used to determine the VIS demarcation threshold reflecting whether the influence is significant. Recursive feature elimination was used to screen the variables related to car-following samples significantly affected by vehicles in adjacent lane. The influence mechanism of variables was determined according to the statistical analysis results. Based on the mechanism proposed， the car-following model suitable for local multi-vehicle environment was constructed and its prediction effect was evaluated. Results show that VIS between the speed of subject vehicle and following vehicle in adjacent lane can characterize the influence and the VIS threshold is 0.668. It is concluded that the attention mechanism and memory effect can explainthe influence mechanism of car-following behavior in multi-vehicle environment. Considering the attention mechanism and memoryeffect， RMSE decrease by 65% in full velocity difference model （FVD） model and 62% in intelligent driver model （IDM） model， MAE decrease by 65% and 59% and R² increased by 180% and 288% respectively， which proved the rationality of the attention mechanism and memory effect in explaining the car following behavior of subject vehicle in local multi-vehicle environment.

There are some disadvantages in the traditional methodology of the mix proportion design of ultra-high performance concrete， such as high cost， low efficiency， and complex process. The application of artificial intelligence technology can help to overcome these shortcomings and intelligently predict various properties， thus intelligent and sustainable mix proportion designs can be delivered. By the review on the research progress of artificial intelligence technology applied in the properties prediction and mix proportion design of ultra-high-performance concrete， obstacles and challenges in current mainstream technologies were pointed out， including data quality， model validation， model interpretability， and multi-objective optimization. In order to resolve these issues， many specific proposals were put forward based on the combination of artificial intelligence technology and discipline theory of building materials.

In order to determine the rheological response of ultrasonically disposed petroleum asphalt， a series of rheological tests were used to analyze the changes in the indexes of three typical petroleum asphalt before and after undergoing ultrasonic disposal. It was found that ultrasound reduces the real-time viscosity of asphalt at high temperature by more than 50%， and has an irrecoverable residual effect； the disposal asphalt becomes slightly softer and the thixotropic limit is increased at mid-temperature； and the creep capacity of the disposal asphalt is reduced at low temperature. Chemical and microanalysis showed that the content of heavy components in the asphalt was significantly reduced， and asphaltene aggregates were homogenized and dispersed. Selective inhomogeneous pressurized heating during ultrasound action induced a cracking reaction in the asphalt， which was responsible for the changes in chemical and rheological properties. This significant physicochemical effect of power ultrasound on petroleum asphalt has the potential to be used in road engineering techniques such as warm mixing， recycling， and modified processing.

The online optimization method was proposed to solve the problem of hybrid integer nonlinear optimal control in hybrid electric vehicle gearing optimization. Firstly， the real-time control method was constructed based on the rolling optimization idea of model predictive control. The objective function was the total weighted cost associated with vehicle dynamic performance， minimum equivalent fuel consumption， and drivability. Secondly， based on the model fitting of the vehicle power system， the analytical solution of energy distribution was obtained by using the minimum principle， and the gear was optimized by the enumeration method at every moment. The simulation results under standard working conditions show that：①the proposed method can improve the calculation efficiency， and the calculation time is less than 50 ms， which has the potential of online application；②Compared with the dynamic programming method， the proposed method can achieve close to the global optimal fuel economy.

In order to understand the effect of ice crystal frost heave on permeable asphalt mixture in cold region， the water-heat-force coupling damage characteristics of permeable asphalt mixture were studied in this paper. Based on the analysis of the initial temperature field and initial water field of typical permeable asphalt mixture， the displacement field， stress field and strain field under the dynamic expansion mode of ice crystal were studied， and the distribution characteristics of the occurrence time of in-situ dynamic expansion and separation dynamic expansion， frost heave stress and deformation in the horizontal and vertical directions were obtained， and the basic change law of the dynamic expansion effect of ice crystal in the permeable asphalt mixture was clarified. The research results provide technical support for the design of permeable asphalt mixture in cold area.

In order to assess the overtaking risk level of intelligent networked vehicles when different networked information was provided. To make up for the neglect of driver factors in traditional risk assessment and the insufficient evaluation capability of a single traffic conflict indicator for complex traffic scenarios， the block maxima （BM） and peak over threshold （POT） methods were introduced to fit the extreme value distributions for the two types of conflict scenarios （follow-me and frontal oncoming conflict） involved in overtaking events， so as to assess the risk of following and frontal collision accidents， respectively. In each conflict scenario， a bivariate extreme value model was proposed to integrate different traffic conflict indicators and a non-stationary extreme value model was proposed to take the driver into account for road safety estimation， and the models were validated with the intelligent and connected vehicle overtaking test data. Extracted the overtaking event from the original test data and calculated the conflict indicators： including the time to collision between the ego vehicle and a preceding vehicle GAP， the time to collision between the ego vehicle and an oncoming vehicle TTC_t₁， the deceleration DRAC， the time to collision TTC， headway with the preceding vehicle THW. The degree of collision risk was characterized by the event probability that the time conflict index is negative or the DRAC is greater than the MADR. The results show that the error results of the binary extremum model constructed by different conflict indicators are different in the head-on collisions， and the binary extremum model constructed by THW&DRAC has the most accurate evaluation results （standard error MAE=0.000 28）. The binary extreme value model constructed by TTC&DRAC is the most accurate （MAE=0.006） in the frontal oncoming collisions. In different conflict scenarios， the non-stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy （the AIC and BIC values are small） compared with the model that does not consider the driver factor. In addition， different intelligent network information （real-time distance， overtaking advice， speed advice） brings different passing maneuvers risks， and when the intelligent network information is speed advice， the overtaking risk of the car is the smallest. Therefore， the non-stationary extreme value model considering the driver factor and binary extreme value model proposed can effectively evaluate the driving risk through the traffic conflict index. Secondly， the experimental data of intelligent and connected vehicles show that the proposed model can accurately assess the overtaking risk level of intelligent and connected vehicles when they provide different intelligent network information.

To promptly detect， evaluate， and address potential traffic risks in highway tunnels， ensuring the safe and efficient operation of tunnels， a dynamic estimation method for tunnel operational risk states was proposed based on spatial-temporal Transformer network. Tunnel traffic flow holographic detection and key cross-section aggregation information as inputs was utilized， the spatial convolution and temporal LSTM was employed by proposed model for unsupervised extraction of spatiotemporal distribution features of different tunnel traffic operational states. Through extensive sample training of Transformer network layer parameters，it aims to capture the distribution and variances of tunnel traffic states in a high-dimensional risk feature space. This facilitates the estimation of operational risk of tunnel traffic flow. The effectiveness of the proposed method is verified by using real tunnel traffic detection data， and the accuracy of tunnel operation risk estimation is about 96%.

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources， and considering the game relationship between user benefits and system benefits in parking allocation， the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then， the optimal parking allocation integer programming model（OPA） considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally， in order to test the validity of the model， the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization， average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

In order to accurately predict the effect of precipitation and ice condensation on the engineering performance of asphalt mixtures during the service of environmentally friendly salt storage asphalt pavements， the long-term road performance changes of asphalt mixture were studied based on immersion freeze-thaw cycle tests. Based on the theory of life reliability and damage principle， considering the influence of moisture and freeze-thaw cycle on salt storage asphalt mixture， the damage evolution model of asphalt mixture under immersion-freeze-thaw condition was established， and the evolution of engineering performance damage of ordinary asphalt mixture and environmentlly-friendly salt storage asphalt mixture was compared. The experimental results show that with the increase of the number of immersion-freeze-thaw cycles， the porosity of the salt storage asphalt mixture increases obviously， while the performance indexes of splitting strength and dynamic stability decrease obviously， and the variation range of the salt storage asphalt mixture is always greater than that of the ordinary asphalt mixture. According to the model calculation and analysis， under the damage of immersion-freeze-thaw， with the precipitation of snow melting salt， the destruction of environmently-friendly salt storage asphalt mixture is developed from the outside to the inside， and the immersion-freeze-thaw cycle has a more significant effect on the splitting tensile strength of the asphalt mixture， which will affect the pavement performance to a certain extent.

The target recognition algorithms and the major problems solved in image classification， detection， and segmentation tasks for side-scan sonar， synthetic aperture sonar， and forward-looking sonar were described. By combining the imaging characteristics and application scenarios of different sonars， the strengths and weaknesses of the target recognition algorithms under the corresponding image processing tasks of the above imaging sonar and the key issues that still need to be addressed were analyzed and summarized， and its future development direction was also looked forward to.

The existing visual object tracking methods only use the target area of the first frame as a template， which makes it easy to fail in rapidly changing and complex backgrounds. To address this issue， this article proposes a Transformer based object tracking algorithm that focuses on the target focus information within the template and dynamically updates the template features. In order to reduce the interference of background information on attention， this algorithm uses a sparse Transformer module to achieve feature information interaction； A template focus attention module was also proposed to focus on the target focus of dynamic template features and initial template features， in order to preserve highly reliable feature information in the initial template. The experimental results show that the success rate and accuracy of our algorithm in OTB100 benchmark testing reach 70.9% and 91.6%， respectively， which are 4.11% and 3.27% higher than similar template update algorithms STARK. This algorithm effectively addresses the limitations of existing visual object tracking methods and improves the accuracy and robustness of tracking.

Addressing the issue that the large amounts of data generated by devices during communication transmission are prone to becoming targets for hackers and malicious users， thereby generating abnormal traffic， and that the sparsity of traffic data makes it difficult to capture the associations between global features， which in turn affects the detection effectiveness of abnormal traffic， a spatiotemporal fusion detection method for abnormal traffic in industrial Internet of Things （IoT） based on the improved bidirectional long short-term memory （BiLSTM） neural network algorithm using mean squared error （MSE） is proposed. Firstly，the industrial Internet traffic data is converted into numerical data through the One-Hot coding method， and the SE mechanism in MSE is used to adjust the weight of traffic characteristics to capture the correlation between global characteristics.Secondly，using the forward and backward LSTM of BiLSTM neural network， the spatiotemporal fusion features of network traffic are extracted.Lastly， and the spatio temporal fusion features are input into the softmax classifier to identify traffic and achieve anomaly detection. The experimental results show that when the number of iterations reaches 30， the loss value of the proposed method can reach below 0.4， when the number of iterations reaches 60， both F1 and Matthews correlation coefficients can reach 60， proving that this method has good overall performance.

To address the issue of large parameter quantities in high-performance image super-resolution networks， a lightweight model was proposed. First， three kinds of large kernel attention were integrated， namely dual-path large kernel attention， large kernel pixel attention and large kernel channel attention， aiming to expand the model perceptual field and establish the long-range dependence of pixels. Second， an adaptive attention fusion mechanism was introduced to enhance the characterization of features and improve the model performance. Experiments demonstrate that the model performs well on visual perception and quantitative tests. On the Urban100 dataset， the mean value of peak signal-to-noise ratio of ×4 reconstruction results was improved by 0.25 dB compared with the currently popular ARRFN algorithm. The reconstructed images have more realistic visual effects， clearer and more natural texture information， which fully demonstrates the proposed algorithm effectiveness.

Based on the YOLOv5 algorithm， the feature extraction process was optimized by introducing the FasterNet lightweight network structure to reduce the computational complexity of the model. Combining Convolutional Block Attention Module （CBAM） attention mechanism to improve the model's attention to defect features. Adopting the SIoU loss function to improve the accuracy of bounding box regression and enhance localization accuracy. The experimental results show that the improved model has achieved significant results on the self built bridge defect dataset， with accuracy and mAP reaching 81.2% and 71.5%， respectively， which is a significant improvement compared to the benchmark model. This study provides technical support for the intelligent detection of bridge defects， which is of great significance for promoting the digital transformation of bridge maintenance management.