When measuring the position of vehicle mass center by Tilting Method（TM）， there would be some problems such as inaccurate calculation of tire force point and large tilting of the vehicle， so Improved Tilting Method（ITM） is proposed in this paper. In the process of measuring the position of vehicle mass center by ITM， the force direction of the tire is always straight up， which does not need to design additional wheel load weighing device and can reduce the influence of adverse factors such as friction. In order to keep the supporting plates horizontal， a parallelogram mechanism is used to constrain them. Based on the balance of static force and moment， the calculation model of ITM is established. By means of numerical simulation， the key components of the system were verified and the measurement process was simulated. The results show that the stiffness and strength of parallelogram mechanism and test bench frame meet the safety requirements. To determine the vehicle mass center， the vehicle titling angle by ITM is smaller than that by TM， which means that ITM can supply higher precision and better experiment safety.

State constraints， input constraints and external disturbances usually exist in the path following problem of wheeled mobile robots. Based on nonlinear disturbance observer， a moving horizon control strategy for path following problem of wheeled mobile robots is proposed in this paper. While there is no disturbance at all， the moving horizon control can satisfy the input and state constraints， and drive the wheeled mobile robot to the desired path. While there are disturbances， in particular， slow varying and “big” disturbances， the proposed nonlinear disturbance observer can estimate the disturbances， and compensate the influence of the disturbances on the wheeled mobile robot through a feedback. Simulation results show that the proposed control strategy can guarantee the convergence of the mobile robot to the desired path under the external disturbance.

Based on Hybrid Particle Swarm Optimization(HPSO), a parameter optimization method is proposed for a Dual Clutch Transmission(DCT) of a multi-mode axle-split Hybrid Electric Vehicle (HEV). Combining the enhanced Particle Swarm Optimization(PSO) and chaotic local search PSO, the HPSO algorithm is proposed to optimize the minimum transmission ratio and partial geometric progression gear ratio of DCT by adopting the equivalent consumption minimization strategy. The optimization is based on a co-simulation platform, in which, the HEV is modeled in AMESim and the control/optimization algorithms are programmed in MATLAB/Simulink. The simulation results show that the optimized transmission parameters can reduce fuel consumption by 2.04%, not only ensuring the dynamic demand, but also having good fuel economic performance.

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.

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.

In order to overcome the shortages of lower efficiency and higher fuel consumption of truck-mounted concrete pump, a global power matching method is proposed based on both fuel consumption rate of the engine and the efficiency of hydraulic pump. The load distributions are summarized by mathematical statistics tool. The fuel consumption rate of the engine is obtained on an engine test platform. Experiments are conducted on the hydraulic pump to find the high efficiency region with different pressure, rotation velocity and displacement. The combination optimization of engine fuel consumption rate and pump efficiency are carried out using genetic algorithm. The engine speed and the pump displacement are jointly adjusted to make output control indexes adapting to the load change in real time, ensuring all the components work in the high efficiency region. Comparative experiments are conducted with new and old methods. Results show that the new method outperforms the old one that the fuel consumption is reduced by 16%.

Regarding the accurate prediction of passenger flow at the entrance and exit of rail transit stations， Considering the land use around the station， Rail transit connection conditions， station attributes and attraction， a two-level passenger flow prediction model is constructed in this paper， station attributes and attraction， a two-level passenger flow prediction model is constructed in this paper， including the passenger flow prediction at the traffic district level based on multiple nonlinear regression and the passenger flow prediction at the entrance and exit level based on the CRITIC method. On the basis of the available data， the total passenger flow of all entrances and exits in the traffic district is predicted， and then be allocated to each entrance and exit. Different types of rail transit stations are randomly selected to verify the effectiveness of the model. The results show that the error between the predicted value of the model and the actual value of the daily entry volume is within 30%， and the average error is 20%， which has a high prediction accuracy.

Based on the central composite design， the three geometric parameters of the air-cooled intercooler shutter fins， the opening angle， the fin spacing， and the louver spacing， are tested with three factors and five levels. Then a complete second-order regression model with the heat transfer characteristics Nu and the flow resistance characteristics f as the objective function is established， and the response surface analysis is carried out to analyze the importance of the influence of each structural parameter， and finally the optimization calculation is carried out according to the multi-objective genetic algorithm. The results show that the heat transfer characteristics are mainly affected by the fin spacing， while the resistance characteristics are most affected by the opening angle. Multi-objective optimization can obtain a set of Pareto solutions， which can be selected by the decision maker according to actual needs.

According to the functional characteristics of Multi-wheel distributed hybrid-driven unmanned frame vehicle （MHUFV）， the vehicle control system is developed. This system includes high-voltage power-on and power-off management based on the coordination of two power sources， fault diagnosis and fault-tolerant control of complex systems with multiple control nodes， and the algorithm design like driver input command determination with dual input mode， anti-skid control， mode switching and energy distribution control. On this basis， the MHUFV software architecture is constructed， and the vehicle controller is developed by using the Rapid Control Prototype （RCP） technology platform. The basic performance of the vehicle is studied by real road test. The results show that the developed vehicle controller can meet the design requirements of the real vehicle， the test results of each key state quantity are reasonable when the full load limit speed is 15 km/h， and the fuel saving effect is 38% compared with the traditional diesel vehicle under the same operating condition， which can meet the development requirements of the current frame car industry.

The dynamic model of articulated vehicle is of great significance for understanding the complex relationship within the vehicle system， predicting the change of vehicle performance and improving the development of new vehicle models. A dynamic model of three-axis semi-trailer with eleven degrees of freedom including vertical and lateral motions is presented. Compared with the vertical ride model， the validity of the coupled model is verified. The wheel bounce and body rolling motion are studied with the changes of road uneven excitation， vehicle velocity， suspension stiffness and tire stiffness of the semitrailer. The research results can provide reference value for the design control and parameter matching of articulated vehicles.

A control strategy of active suspension systems was proposed for high-mobility rescue vehicles—model predictive control strategy based on ensemble Kalman Filter technology（EnKF-MPC）. Firstly， dynamic model of the active suspension system was completed for high-mobility rescue vehicles. The data of the vehicle dynamic system and the vehicle-mounted positioning system were fused through the Ensemble Kalman Filter technology in order to realize the accurate estimation of the vehicle's pose information； Aiming at the problem of vertical positioning error for the vehicle positioning system， a point cloud matching algorithm was designed to complete the accurate evaluation of the vehicle's vertical direction information； in addition， a model predictive control strategy was proposed， which used the vehicle's pose information obtained by the ensemble Kalman filter algorithm and the road profile information obtained by the on-board lidar as system inputs to control the active suspension system of the vehicle to improve the ride comfort and handling stability of the vehicle. Finally， a real vehicle test was carried out. The research results show that the vertical direction error of the proposed vehicle pose estimation algorithm is about \pm \mathrm{3.100}\text{?}\mathrm{c}\mathrm{m}， the pitch angle error is about ±0.175°， and the roll angle error is about ±0.210°. Compared with the passive suspension system， the proposed active suspension control method reduced the root mean square value of the vertical displacement by 37%， the pitch angle by 35%， and the roll angle by 35%， which significantly improved the ride comfort and handling stability of the vehicle.

Based on the methods of CAE,bench testing and vehicle torsion test,the torsional mechanism of vehicle was revealed through using the normal condition.The research promotes the precision of the simulation value of the body torsional rigidity and the testing value by using the theory of non-linear interpolating,and the torsional stiffness of body and the torsional stiffness of suspension were systematically analyzed,and the theoretical basis and test methods for the static torsional stiffness of whole vehicle were provided,namely,the torsional stiffness of the equipped state body is 1.72 times than BIW,the proportion of the front suspension torsional stiffness and rear suspension is 1.44,the torsional stiffness of BIW is 5.8 times than suspension,the torsional stiffness of the equipped state body is 11 times than the suspension,ande the torsional stiffness of the equipped state body is 1.89 times than BIW.On the basis of the theory study,three matching principles are explored.It provides theoretical basis and design methods for the passenger car.The research results have the promotional value of practicability and a wide range of engineering application.

In this paper， the simulation conditions of pitching motion are determined， and the external flow field of the whole vehicle under various conditions is simulated and analyzed. The aerodynamic characteristics under various pitching conditions and the interaction between aerodynamic components are explored. The reasons for the differences of aerodynamic result between various pitching conditions are obtained by analyzing the changes of flow field around the vehicle body. By analyzing the simulation data， it can be seen that the aerodynamic negative lift of the car under all pitching conditions is mainly provided by the body， front wing and tail wing， while the front and rear wheels mainly provide aerodynamic lift. The aerodynamic drag of the car is mainly generated by the body and tail wing， and the sum of the two accounts for more than 75%. The sum of the aerodynamic drag of the front and rear wheels accounts for only about 10%. When the car body attitude changes in pitch， the changes of front wing， tail wing， ground clearance and the interaction between aerodynamic components lead to the change of the aerodynamic characteristics of the whole car. The results show that under the condition of pitch angle -1.0°， the aerodynamic lift drag ratio of the whole vehicle is the largest， the aerodynamic efficiency of the car is the highest， the absolute value of the total aerodynamic negative lift is the largest， the total aerodynamic resistance is relatively small， and the aerodynamic pitch torque is the smallest. Under this condition， the performance of the car is the best.

As a classic multi-level optimization algorithm， the Canny algorithm is widely used in MRI edge detection. MRI has the limitations of uneven gray density and low contrast. This paper uses lumbar disc MRI as an example to propose an improved Canny algorithm for optimization in edge detection. First， the image contrast is enhanced， and on this basis， the median filtering is introduced to effectively process impulse noise for preprocessing. Then， for the problem of insufficient edge extraction accuracy， the gradient direction template is increased to obtain the gradient amplitude and direction. For the excessive number of false edges and edge discontinuities， the gradient intensity information calculation method is used to achieve threshold adaptation. Finally， the image enhancement is performed again through the histogram normalization. This paper uses four evaluation criteria of PSNR， SSIM， MSE and algorithm running time to verify the traditional algorithm， the existing algorithm and the improved algorithm in this paper. The results show that the improved Canny algorithm significantly improves the accuracy of MRI detection， effectively reduces the false edges， and it is adaptive. The results of this article have certain reference significance for MRI in medical image processing.

An electromagnetic noise test system based on MSP430 and Field Programmable Gate Array (FPGA) dual controller in the larmor frequency range 1 kHz ~ 3 kHz is designed. The system reduces equivalent input noise by the parallel low noise operational amplifiers. The system realizes the controllable passband and adjustable 128 gain level to get more information about the current environment of electromagnetic noise. NIOS II soft core built in FPGA is used to set up data acquisition system, and the data are archived in the SD card by the SPI bus so as to realize effective data acquisition and storage. Analysis of the indoor and outdoor measured data verifies the effectiveness of the system.

To analyze the risk-causing mechanism of different freeway characteristics， this paper introduced the microscopic kinetic parameters of the vehicle under normal driving conditions. The standard ordered Logit model and the binary Logit model were conducted to explore the relationship between the microscopic kinetic parameters and road characteristics （e.g.， diversion areas， merging areas， and curve segments）； and the relationship between the microscopic kinetic parameters and crash risk （e.g. rear-end， fixed-object hitting， and overturning）. Then， taking the micro-kinetic parameters as the link， the effects of different road characteristics on crash risk is independently quantified. The results indicate that the longitudinal kinetic factorcan effectively map the level of rear-end risk， and the difference in fluctuations of can be attributed to the diversity of road characteristics. That is， the longitudinal kinetic factor can be used as an intermediate variable of risk transmission to realize the determination of the level of crash risk on freeways， and at the same time， provide help for the identification of road dangerous defects. Compared with the straight-line section without a diversion area， the complex feature of the "diversion area" has the greatest effect on the added risk of rear-end crashes， followed by "composite alignment"， and the "horizontal curve" is the weakest. The greater the length of the transition curve of the horizontal curve section， the higher the risk of rear-end crashes. In addition， the paper also finds a significant risk compensation phenomenon， suggesting that a more relaxed driving environment may cause drivers to relax their vigilance and increase the risk of rear-end. The conclusions can provide theoretical support and experience for the prevention of crashes and the formulation of rescue measures for a specific type of crash.

Text input is the most common interaction behavior in viture reality（VR） environment， and the mainstream text input is currently realized by laser pointing. However， the existing methods have many drawbacks， such as low efficiency， large jitter， and easy false trigger， which cannot be used to frequently input text in VR environment. Therefore， a novel text input method for VR environment is proposed. First，First, partition the keyboard, use the handle to select the area where the characters are located， and use the word disambiguation algorithm to realize text input in units of words； secondly， perform cluster analysis on the user's click coordinates to do one-key multi-word processing； Finally， three keyboard layouts that conform to user habits are designed， and the optimal layout is determined The experimental results show that the typing speed of the optimal layout is 13.44 WPM（Words Per Minute） with an accuracy of 92.26%， which is a significant improvement compared with other input methods.

A robust fundamental matrix estimation algorithm based on kernel fuzzy clustering is proposed. The residual features of match points are detected by this algorithm. Using the kernel functions, the nonlinear dividable features in the original one-dimensional space can be mapped to a high-dimensional feature space, in which the clustering can be performed effectively. The match points are classified into inliers and outliers by the fuzzy clustering method. The inliers and the outliers are modeled by Gaussian function, and the judgment values of the divisibility of the two sets are defined and calculated. The iteration will continue until the judgment value converges. Experimental results of the fundamental matrix computation on both simulated and real data demonstrate the superiority of the proposed algorithm in precision and efficiency over classical RANdom SAmple Consensus (RANSAC) algorithm.

In order to improve the mixed traffic of connected automated vehicles and human-driving vehicles， this paper proposed a collaborative optimization method for signals and trajectories of connected automated vehicles on dedicated bus lanes. In the double-layer optimization model of signal and trajectory， this paper builds linear mathematical relations between trajectory variables and traffic signal variables， and compute the solutions in sections of time and space. Consequently， this model is used in real number case to verify the validity and operate comparative experiments between collaborative and non-collaborative optimization. It proves that efficiency of connected automated vehicles in collaborative optimization is 7.7% higher.

The subspace pursuit-polynomial chaos expansion method （SP-PCE） was proposed for the uncertainty quantification of EV-WPT system's transfer efficiency. Firstly，by establishing the three-dimensional electromagnetic simulation model of EV-WPT system and reasonably setting the distribution type of relevant variables， the statistical characteristic parameters such as mean， variance and probability density curve that can characterize the uncertainty of EV-WPT system's transfer efficiency were calculated by SP-PCE method. Then， combining the SP-PCE method and Sobol method to carry out the global sensitivity analysis to obtain the influence degree's quantitative index of random input variables on system's transfer efficiency. Finally， the accuracy and efficiency of the proposed method were verified by numerical simulation experiments， which provides a theoretical basis for ensuring the high transfer efficiency of EV-WPT system as well as system's structure optimization.

Through the research of 6-PSS parallel manipulator, this paper developed the kinematics model based on the relationship between spherical joints' spatial positions in three kinds of coordinate systems, which respectively are the global coordinate system, the local coordinate system and the hinge-point coordinate systems fixed on sliding blocks. A composed numerical method based on multivariate Newton's method and the steepest descent method was proposed. The theories of numerical analysis and machine computation were used to improve the convergence speed and decrease the amount of operations in a single-step iterative process. Finally, the forward kinematics solution program was written for the numerical simulation. The results show that the improved composed numerical method can not only decrease the computational time and converge to the solution accurately, but also reduce the dependence on the initial estimate and expand the convergence range. This method can be effectively applied in robot system based on 6-PSS parallel manipulator.

Considering the limitation of insufficient accuracy and poor stability of multi?sensor traffic data fusion algorithm, a mind evolutionary data fusion algorithm based on BP neural network is presented. Based on the introduction of the basic principle, the calculation steps of the proposed fusion algorithm for optimizing the initial weight and threshold of network are designed by using the “convergence” and “alienation” operations. This makes it possible to find out the best fusion model. The empirical analysis using the interval velocity indicates that the proposed fusion algorithm shows better convergence and has better accuracy and stability than the BP neural network algorithm, which are improved by 9.38% and 31.44% respectively. Time sensitivity analysis shows that the proposed fusion algorithm also has good transportability.

In order to improve the efficiency of the multi-object tracking method， a joint detection-apparence network based on anchor aligned convolutional feature， called AAC-JDAN， was proposed. On the basis of the object detection network YOLOv3， an anchor transformation network and an anchor aligned convolutional operation was introduced， so that the network can detect the rotated objects， while alleviating the problem of the weak correlation between the apparence feature extracted by the exsiting joint network and the rotated objects； by adding an apparence feature extraction branch in the detection network， two subtasks of object detection and object apparence feature extraction were combined in a multi-task joint learning manner to realize the sharing of the low-level feature， and the apparence feature vectors can be extracted along with the corresponding detected objects， which improves the overall efficiency of the tracking algorithm. A fast online data association method was proposed to realize the efficient tracking of multiple rotated objects in the video. The similarity matrix between the incoming detections and the trajectories was calculated with the object apparence feature extracted by AAC-JDAN and the motion prediction result given by the Kalman filter， and the matching was done by the KM algorithm. When tested on two public datasets and a custom dataset， the TPR， MOTA， and IDF-1 reached 80.4%， 71.3%， and 69.5%， respectively， and the framerate reached 20 frames per second， this showed that the proposed method achieves a better balance in the speed and accuracy of tracking.

Aiming at the current situation that the state strongly advocates farmers to plant forage Broussonetia papyrifera， but there is a lack of suitable harvesting machinery in China， this paper combines the harvesting characteristics of Broussonetia papyrifera， guided by the moving track of hob， the limit value of cutting stubble height of Broussonetia papyrifera and the requirements of conveying roller for stem grasping， and takes neat stubble without splitting， low complete harvest loss and smooth branch and leaf grasping and conveying as the technical requirements， a hob rotary mechanical harvesting header for Broussonetia papyrifera was developed. According to the box Behnken experimental design method， the regression model of the number of hobs， hob speed and machine forward speed on the consistency of stubble height and harvest loss rate is constructed. The response surface method is used to analyze various factors and their interaction laws， and the parameters of the regression model are optimized. The optimal parameter combination is obtained as the number of hobs 3， hob speed 889.673 r/min and machine forward speed 4.138 km/h， the consistency of stubble height was 96.482%， and the harvest loss rate was 0.686%. The verification test is carried out under the optimal parameter combination. The results show that the consistency of stubble height is 97.12%， the harvest loss rate is 0.67%， and the test value is close to the predicted value. This study can provide a reference for the design of mulberry harvester.

In current augmented reality systems， the most popular touch input method suffers from inefficiency and occlusion. To address these challenges， this paper proposes a novel text entry method that combines gesture keyboard with an imaginary keyboard， where users draw gestures in a transparent area. First， we verified that it is feasible and practical to use an imaginary keyboard in an augmented reality environment. Second， we conducted a user study to evaluate the performance of three entry methods： touch， gesture with visual feedback and gesture without visual feedback （the method of this paper）. The results show that the average speed of the proposed method can reach 16.95 WPM （Words Per Minute）， which is 6.92 WPM higher than touch input.

In order to better obtain shortwave digital signals due to noise interference in the channel， a modulation recognition algorithm for shortwave digital signals under low signal-to-noise ratio is proposed. The short wave digital signal model to be identified is constructed， and the transmitted signals are collected to obtain pulse noise， Gaussian noise， and intersymbol interference signals. The roll off and the amplitude frequency response of the Matched filter are calculated using instantaneous values， and the noise effect is suppressed by combining phase correction. The instantaneous information is optimized by wavelet filtering technology， and the average of instantaneous amplitude， instantaneous phase and Instantaneous phase and frequency absolute value is calculated to complete modulation recognition of short wave digital signal. Through experiments， it has been proven that the proposed algorithm can effectively achieve digital signal modulation recognition with minimal noise impact and high accuracy.

In the vehicle network environment， the traffic system will present the mixed coexistence of intelligent connected vehicles and traditional artificially driven vehicles for a long time. Focusing on the new mixed traffic flow in the intelligent connected traffic environment， the decision-making model of vehicle lane change behavior was established. The dynamic risk model during lane-changing is introduced to establish the interaction between autonomous vehicles and traditional vehicles in the mixed traffic flow. The lane-changing behavior of autonomous vehicles in mixed traffic flow is modelled based on game theory. Lane-changing behavior between autonomous vehicles is the nature of the non-cooperative game. Vehicles seek for a lane with better driving conditions by taking their own driving state as the game benefit. The simulation results show that the game lane-changing model has higher lane utilization and safety stability than the traditional gap threshold acceptance model.

The 3-degrees of freedom （DOF） helicopter has three typical features： nonlinearity， underactuation， and suffering from uncertainties and external disturbances. It has received much attention from well-known research groups， owing to its convivence for the verification of motion planning and robust control. However， the lack of accurate models for motion and actuator dynamics leading to the limitation for the motion planning and control algorithms. Motivated by this fact， we study the model of a 3-DOF helicopter platform as a multi-rigid-body system， which is equipped with low-precision encoders for attitude measurements. The detailed work includes： ① modelling the dynamics for three bodies respectively； ② identifying the parameters of the multi-channel motion as well as the parameters of the motor-propeller lifting component； ③ linearizing and analyzing the nonlinear model； ④ verifying both the nonlinear and linearized models. Finally， a benchmark model for the platform is obtained， by which， the model-based controller can be designed. Some experiments are designed to show the completeness and high-accuracy of the benchmark model. Two “feedforward + linear quadratic regulator （LQR）-based feedback” controllers for the angular trajectory tracking are designed based on the identified model and implemented on the experimental setup， where the tracking performance of these controllers can be benchmarked against other advanced algorithms.

In event-based social network， users and activities construct heterogeneous network naturally. We propose an end-to-end recommendation model using high-order connectivity in heterogeneous network and non-linear matching relations to increase hit ratio. First， we extract the effect of high-order connectivity of each node， and get the embedding of nodes by graph neural network. Then， the embedding of user-activity pair is input of the multilayer perceptron to gain the current matching probability. Finally， by repeating this process the final matching probability is obtained. Experiments show that， the recommendation method is stable. Comparing with existing algorithms， our algorithm increases hit ratio more than 10%， and normalized discounted cumulative gain about 10%. Comparing with using multilayer perceptron only， both hit ratio and normalized discounted cumulative gain rise when considering high-order connectivity information.

In the process of constructing the knowledge graph, the existing method ignores the processing of semi-structured data, which leads to the inaccuracy and time-consuming in construction of the knowledge graph. Therefore, an automatic knowledge graph construction algorithm based on massive text data is proposed. A triplet extractor is used to extract massive text data sources, and to extract semi-structured data, while eliminating redundant data. According to the data processing results, the appropriate data objects are selected using the data collection function as the text data source constructed by the knowledge map. The data source is subjected to standardized processing such as text format conversion, word segmentation and feature extraction. The underlying semantics of the data are analyzed and an XTM visualization map is drawn to form a preliminary knowledge map. The triples of users, ratings and items are composed by mining the existing knowledge in this knowledge map, applying potential vectors to information recommendation, and the graph evolution algorithm is used to predict the ratings, users and items, constructing latent vector models Domain recommendation to realize the automatic evolution of the knowledge graph. Experimental results show that the algorithm has higher construction accuracy and less time consumption, which shows that the algorithm is reliable and practical.

A deep reinforcement learning agent based on Deep Q-Network （DQN） algorithm was constructed to solve the problem that the state machine decision model cannot effectively deal with the rich context information and the influence of uncertain factors in the snow and ice environment. The motion planner was used to augment the agent， and the rule-based decision planning module and the deep reinforcement learning model were integrated together to build the DQN-planner model， so as to improve the convergence speed and driving ability of the reinforcement learning agent. Finally， the driving ability of DQN model and DQN-planner on ice and snow road with low adhesion coefficient is compared based on CARLA simulation platform， and the training process and verification results are analyzed respectively.

Combined with the temperature change and icing in the process of fuel cell cold start， a control oriented third-order fuel cell cold start model was established. Aiming at the unmeasurable ice volume fraction of cathode and anode， an ice volume fraction estimation method based on extended state observer was proposed. On this basis， according to the characteristics of constraint and coupling nonlinearity in the cold start process of fuel cell， an optimal control method of fuel cell cold start system based on nonlinear model predictive control was proposed， which realizes the double optimization objectives of improving the rapidity of cold start system and reducing hydrogen consumption. Finally， simulation experiments verify the effectiveness of the designed optimal control system of cold start system.

In order to overcome the defect of fixed safety distance in Optimal Velocity (OV) model and its related derivative generalized force (GF) model and full velocity difference (FVD) model, a car-following model with improving safety distance based on the OV model is proposed in this paper. The improvements of the proposed model are analyzed theoretically. Then, according to the proposed model, the simulation frameworks are developed to analyze the impact of three model parameters on traffic flow stability. These parameters are the density of traffic flow on the road, the maximum velocity for driving vehicle and the time of driver's reaction delay. The simulation can truly reproduce the propagation state of traffic flow. Finally, using the data from U.S. NGSIM Database as samples, the accuracy and effectiveness of the model presented in this paper are verified after testing with cluster analysis by comparing with OV model, GF model and FVD model.

In order to study the viscoelastic damage characteristics of sandstone， the Hopkinson test under multiple impact compression was carried out. Based on the dynamic stress-strain curve of sandstone， two definitions of dynamic deformation modulus in the range of middle strain rate are analyzed. It is shown that the equivalent dynamic deformation modulus can reflect the damage characteristics of sandstone under dynamic impact compression. Microscopically， the statistical damage variable of sandstone can be defined by the ratio of the number of damaged microelements to the total number of microelements； and macroscopically it can also be defined by the dynamic deformation modulus before and after the microelement failure. It is verified that the dynamic deformation modulus obeys the Weibull statistical distribution. The force model of the microelement is composed of a viscous body and a damaged body in parallel. The mechanical mechanism of the viscoelastic body is reasonably explained by the highly linear correlation between the dynamic stress， the dynamic strain and the strain rate of sandstone. The stress-strain relation of the damaged body is derived based on the Drucker-Prager strength criterion and the strain equivalent principle proposed by Lemaitre. Through the verification of the introduced viscoelastic constitutive relation curve and the test curve of sandstone， the trend of change between them is consistent， which has good representativeness， indicating that the established viscoelastic damage constitutive relation is reasonable. The deviation between them can be solved by increasing the number of sandstone samples， optimizing the test scheme and applying probability statistics. After obtaining the viscoelastic constitutive relationship parameters with a certain guarantee rate of probability， the viscoelastic damage constitutive relation can be used to analyze and evaluate the stability of the rock slope in the coal mining area， and provide the necessary theoretical analysis basis for the related geological engineering evaluation of safety.

To meet the requirement of the working performance and security of automated warehouse, the mathematical model of multi-objective slotting allocation optimization is constructed. The outbound-inbound operation time, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products are taken as the multi-objective functions. A Simple Weighted Genetic Algorithm (SWGA) is used to solve the model obtained by weighted and normalized multi-objective models. To avoid the problem of premature convergence, a Multi-population Genetic Algorithm (MPGA) is proposed to solve the optimization model of slotting allocation. Combined with experiment of toy car assembly and experiment of automated warehouse, the results of slotting allocation are obtained. The effectiveness of the MPGA is verified by comparison and analysis of the two results. This study provides an effective solution for automated warehouse slotting allocation and optimization.

Due to the fact that the existing domestic technologies applied for practically monitoring the cold-chain are fairly limited, the loss of the cold foods during the logistics is much higher than that of international standards. To solve this problem, a new method for monitoring cold chain logistics was proposed and developed based on RFID, GPS and GPRS technologies,which can be used to monitor the condition of fresh foods in real time and publish the condition and quality of the cold foods on-line, including the parameters of the temperature, picture and location information of goods in transit. The warning message will be automatically sent to the drivers and centrally control office when the temperature exceeds a given threshold. The laboratory experiment results have shown that this system can be used to ensure that the goods in transit is always maintained in a safe condition, the loss of the foods during logistics can be effectively reduced.

In order to study the characteristics of lane changing conflicts involving trucks on the expressway， reduce the risk， and explore the significant factors that cause traffic conflicts among lane changing behaviors of trucks. Firstly， this paper considered the traffic conflict measurement indicators of time and space dimensions， proposed judgment indexes， and used the extracted traffic conflict indexes as a category of samples in the conflict prediction model. Secondly， the XGBoost algorithm was adopted to build the prediction model， and its performance was compared with the Random Forest （RF） and Gradient Boosting Decision Tree （GBDT） algorithms. Finally， the SHAP algorithm was used to study the impact of traffic conflicts on truck lane changes， including the lane changing characteristics of trucks， the relative motion state between different vehicles， and traffic flow. The results show that the XGBoost model has a better prediction effect， with an accuracy and F1-score of 83.87% and 84.85%， respectively. The lane changing safety of trucks is greatly affected by the motion status of the preceding vehicle in the original and target lanes， and the occurrence of collision is negatively correlated with the vehicle length and traffic volume of the road section per minute. Under the interaction of multiple factors， when the traffic volume of a section is more than 70 pcu per minute， the probability of collision when trucks change lanes increases with the increase of the proportion of trucks in the traffic flow. When the vehicle length is less than 8 m， the possibility of truck collision is positively correlated with the speed along the lane line.

In order to study the safety of pedestrians crossing the street during the second conflict between people and vehicles at signalized intersections， three signalized intersections in Kunming City， Yunnan Province were selected as the research objects， and the traffic conflict analysis software T-Analyst was used to obtain vehicle trajectory data and traffic conflict data. A multi-angle safety evaluation method was adopted， based on the DV framework to judge the initial situation of the person-vehicle conflict， the traffic conflict indicators PET_S and PET_H， the maximum rejection gap and acceptance gap， and the average crossing speed to analyze the safety of pedestrians crossing the street. The results show that compared with the secondary conflict， the initial conflict situation faced by pedestrians in the primary conflict is more dangerous， and the frequency of pedestrians choosing to cross the street at risk is also higher. There is no significant difference in the secondary human-vehicle conflict in the indicators such as vehicle crossing behavior， traffic conflict indicators PET_S and PET_H， maximum rejection gap， acceptance gap， and critical gap. However， the average crossing speed of the secondary conflict was significantly higher than that of the primary conflict， and the potential risk of pedestrian crossing was higher in the secondary conflict.

The application of location information generates a large volume of spatio-temporal data with multidimensional attributes such as latitude， longitude and time. The index-based trajectory query method cannot obtain the spatio-temporal semantic features of the trajectory， and its trajectory representation ignores the spatio-temporal correlation of the trajectory and is inefficient for large-scale trajectory data query. To solve the above problems， a kind of trajectory encoder based on sparse multi-head attention is proposed， through the trajectory of the encoder can extract high-level semantic features， trajectories are represented as encoding vectors. On the basis of trajectory coding vector， a trajectory query method based on locally sensitive hash function is developed， which can query large-scale trajectory data quickly. The experiment results show that the proposed trajectory query method can effectively deal with kNNT query problems and it is superior to the current trajectory query method in terms of accuracy and efficiency.

The vehicle behaviors of three frontal collision modes, including the full frontal collision, the oblique collision, and the offset frontal collision, were discussed. Taking a certain minibus having met the requirement of the full frontal collision as an example, its dynamic response of the structure under 30° oblique collision and 40% offset frontal collision were analyzed by the FEM method according to the requirements of the regulations FMVSS208 and ECER95. Comparing with the simulation results under the full frontal collision, the measures of improving the overall crashworthiness of the minibus and its evaluation parameters were proposed and validated.