To improve the stability and accuracy of the sound quality evaluation model, the vehicle interior sound quality evaluation model(ALSTM) based on long short-term memory (LSTM) network and attention mechanism was proposed. The steady noise samples of different brands of vehicles at the right ear of drivers under different working conditions were collected, and the subjective evaluation test of noise samples was carried out with annoyance as evaluation index to establish the evaluation data set of interior sound quality. On the basis of the data set, the sound quality evaluation model based on LSTM network was constructed with Mel-scale frequency cepstral coefficient (MFCC) of noise samples as feature input, and the attention mechanism was introduced to optimize the model. The experimental results show that the proposed evaluation model of sound quality can effectively evaluate the vehicle interior noise, and the accuracy in the test set is as high as 97.07%. Compared with other methods, the stability, convergence speed and classification accuracy of the ALSTM model are improved.

The robust backstepping sliding mode RBF network adaptive controller was proposed for the quadrotor unmanned aerial vehicle(UAV) attitude system with disturbance. Based on the backstepping sliding mode control, the RBF network was used to approximate and compensate the ideal control law. The minimum parameter learning method of the neural network was adopted, and the weight upper bound of the neural network was estimated as estimated value of the neural network. The adaptation law was used to replace the adjustment of neural network weights, and Lyapunov theorem was used to prove the stability of system. The simulation results show that compared with the backstepping sliding mode control method, the proposed method has shorter adjustment time and better tracking accuracy in the case of disturbance. It is verified that the proposed method has better anti-interference and robustness.

To solve the torque distribution problem of pure electric commercial vehicles driven by dual motors, the fuzzy control strategy based on particle swarm optimization(PSO) was proposed. The physical model of the vehicle power system was established in Simulink/Stateflow software, and the vehicle torque was distributed based on PSO algorithm. Due to the large amount of calculation and being not suitable for the real vehicles, according to the calculation results of PSO algorithm and the traditional project experience, the fuzzy controller with real-time parameter adjustment was designed to carry out torque distribution with high running speed and basically global optimization effect of PSO. The verification and analysis results show that compared with the original single motor power system, the energy consumption of the dual motor power system is reduced by 12.08%. Compared with the average distribution control strategy, the total motor loss is reduced by 13.09% in dual motor power system.

To solve the problem of vehicle dynamics state variations in autonomous vehicles due to the uncertainty of road conditions on seacrossing bridges, the vehicle roll control strategy was proposed based on the deep deterministic policy gradient (DDPG) algorithm, and the generalization capability under different speeds was discussed. The vertical model of the seacrossing bridge was constructed to provide dynamic road environment. The vehicle dynamics and vehicle tracking error models were established for incorporating the dynamic characteristics of vehicle roll, sideslip and yaw and for establishing the criteria for roll stability. The state space and action space for the DDPG algorithm were designed, and the reward function was formulated based on the vehicle roll state. The numerical simulation results show that by the DDPG algorithm, good performance is achieved in each episode with robust learning and problemsolving capabilities in complex and uncertain environments. The vehicle roll angle and lateral distance error are ensured within acceptable and minor fluctuation ranges to achieve safe vehicle control.

Based on the real vehicle configuration driven by parallel hub motor, the main factors affecting the braking energy recovery efficiency were analyzed, and the Federal Kalman filter longitudinal speed estimation algorithm was used to propose the three-layer compound braking control strategy. By the braking decision-making layer, the braking conditions were identified and entered into the corresponding braking mode according to the pedal input and driving state. According to the decision, by the braking control layer, the electric braking torque distribution of the front and rear wheels was optimized through particle swarm optimization (PSO) under conventional braking conditions to maximize the effective battery recovery efficiency. Under emergency braking conditions, the fuzzy self-tuning PID algorithm was used to realize the safe and effective control of the slip rate of each wheel on different adhesion roads, and the robustness of the anti lock braking control was optimized with improved braking safety. The command of the control layer was responded by the braking executive layer, and the electric braking compensation mechanism was designed to quickly compensate and adjust the pressure error of each wheel cylinder for improving the braking stability of the vehicle. The control strategy was verified by the real vehicle.

To solve the problem of excitation trajectory design in the six-axis robot dynamic parameter identification, the improved differential evolution (IDE) algorithm was proposed to optimize the excitation trajectory parameters. The dynamic model of the six-axis robot was established by Newton-Euler iteration method, and the condition number of the minimum inertia parameter observation matrix was taken as optimization objective function. By improving the differential evolution algorithm, the inverse optimal worst strategy was introduced to improve the initial value of the population, and the adaptive algorithm was used to improve the mutation factor and crossover factor. The improved differential evolution algorithm was used to optimize and design the Fourier series for meeting the constraints of robot and as excitation trajectory for identifying the parameters of robot. The experimental results show that the excitation trajectory designed by the proposed optimization method can give full play to the dynamic characteristics of robot and improve the anti noise ability of robot dynamic parameter identification experiment, which can provide foundation for establishing accurate robot dynamic model.

To solve the problems of privacy leakage and data tampering in the storage and management of user identity data on existing centralized crowdsourcing logistics platforms, the trusted identity authentication model based on the unspent transaction output(UTXO) model blockchain was proposed. The implementation of identity registration, identity verification, identity revocation and identity authentication was realized in the model by blockchain technology. To solve the risks associated with storing user privacy data on the blockchain, the hybrid encryption and decryption scheme was adopted to ensure the secure storage and sharing of personal information on the chain. For the issue of platform user identity authentication, the trusted identity credential was used to log into the crowdsourcing logistics platform by the zero-knowledge proof method. The security of identity information and authentication was analyzed, and the security performance of the proposed method was compared with those of the LIU and LI methods. The experiments were completed in Python 3.9 environment, and the identity information encryption and zero-knowledge identity authentication were tested and analyzed. The experimental results show that the authentication latency and transaction processing capability of the proposed scheme are superior to those of the existing SHAO scheme.

The connection fault characteristics of parallel battery modules were analyzed by the designed connection fault testing method， which showed that the larger the connection fault resistance was, the larger the terminal voltage deviation was. The voltage and branch current characteristics at different connection fault degrees were explored by the fault simulation model. The results show that the closer the fault location to the load connection point is, the smaller the impact on the uneven distribution of each branch current is. The characteristics of voltage, current and incremental capacity (IC) curves at different connection fault degrees indicate that there are significant differences in the branch current. The characteristics of the IC curve vary greatly at different C-rates. The peak Ⅱ is relatively stable, which has correlation with the connection resistance.

To investigate the photo-thermal conversion performance of TiN nanofluid in the direct absorption solar collectors（DASC）, the ethylene glycol (EG) based TiN nanofluid with polyacrylic acid (PAA) stabilizing ligand was prepared. The long-term stability of the TiN-PAA nanofluid was characterized by conducting the static storage test at ambient conditions to show that the average transmittance of the TiN-PAA nanofluid with 100 mg/L was increased by 1.908% after ambient storage for 310 days. The photo-thermal conversion performance of the TiN-PAA nanofluid in DASC was evaluated. The effects of nanoparticles concentration, mass flow rate and irradiation intensity on photo-thermal conversion performance of the collector with TiN-PAA nanofluid were discussed. The results indicate that the stagnation temperature and efficiency of the TiN-PAA nanofluid are much higher than those of the base fluid EG after 2.0 h irradiation, and the stagnation temperature of TiN-PAA nanofluid with 100 mg/L is 80.12 ℃. The efficiency of TiN-PAA nanofluid is improved with the increasing of mass flow rate, and the efficiency of 75 mg/L TiN-PAA nanofluid is increased by 32.69% as the mass flow rate is increased from 1 mL/min to 3 mL/min. However, the efficiency of TiN-PAA nanofluid is decreased as the irradiation intensity is increased. The efficiency of the 75 mg/L TiN-PAA nanofluid reaches 69.10% when the irradiation intensity is 800 W/m2.

 To solve the severe miss detection problem in pedestrian detection caused by insufficient pixel information of distant targets and occlusion induced loss of human pattern information, the pedestrian detection method was proposed based on dual key points combinations. The discriminative semantic features of pedestrians were effectively extracted and fused by utilizing key points of the head and center regions for significantly reducing the pedestrian miss detection rate. The deformable convolution was introduced into the deep aggregation backbone feature network to enlarge the receptive field and enhance the semantic information of human pattern. The dual branch joint detection module based on key points combinations was designed, and the positive samples for different branches were redefined to strengthen the semantic information of small scale and occluded targets. The results of the dual branch detection were fused using the non maximum suppression (NMS) algorithm. The results show that on the CityPerson validation dataset, the average miss detection rates of the normal, small scale and heavily occluded subsets reach 8.24%, 11.81% and 30.59%, respectively. Especially, for the heavily occluded subset, the miss detection rate is reduced by 15.71% compared to the traditional method ACSP. By the proposed method, the detection speed reaches 16 frames per second. On the CrowdHuman dataset, the average precision and average miss detection rate are 86.30% and 45.52%, respectively. Compared with other state of the art methods, the proposed method exhibits superior performance in average precision, miss detection rate and detection speed, which demonstrates significant application value in complex scenarios with dense pedestrian crowds.

Under nitrogen atmosphere, the thermogravimetric tests were carried out on rape straw, polypropylene, acrylonitrile butadiene styrene resin (ABS resin) ， rape straw/polypropylene  with mass ratio of 1∶1 and rape straw/ABS resin with mass ratio of 1∶1 at temperature range of 30-800 ℃ with heating rate of 20 ℃/min. The apparent activation energy was measured by Friedman method， and the kinetic analysis was also carried out. The catalytic pyrolysis of rape straw and the cocatalytic pyrolysis of rape straw/polypropylene and rape straw/ABS resin were tested, and the threephase yield was calculated and analyzed. The results show that there is interaction among the copyrolysis reactants, and blending is conducive to reducing the difficulty of pyrolysis reaction and improving the spontaneity of reaction. According to the GC/MS analysis, the highest yield of monocyclic aromatic hydrocarbons is about 22.11% when the blending ratio of straw and polypropylene is 1∶1, and the highest yield of monocyclic aromatic hydrocarbons reaches 32.87% when the blending ratio of straw and ABS resin is the same.

To solve the problems that the current segment-based video abnormal event detection model with only considering the normal or abnormal of segments could lead to the abnormal fragmentation, the abnormal start/stop location spreading and the difficulty of abnormal event classification, the video abnormal event detection and classification method was proposed based on salient features and space-time graph network. The video anomaly temporal fragment integration and refinement method based on space-time fusion graph network was proposed to integrate the continuous anomaly regions. Considering the feature transferability of the space-time fusion graph network, the anomaly regions were refined to effectively solve the problems of uncertainty in anomaly discrimination and fragmentation of anomaly fragments. To solve the difficult classification problem of weakly supervised anomalous events caused by the intrinsic features of difficultly effective expressing, the abnormal event feature learning and classification method was proposed. The feature similarity graphs and abnormal similarity graphs were established in abnormal regions, and the graph convolutional networks were used for the abnormal event feature fusion learning. The abnormal-imbalance loss function was designed to improve the classification performance of abnormal events. The results show that by the proposed method, the AUC reaches 85.37% on the UCF-Crime dataset, which is 9.83% higher than the baseline of SULTANI method and 0.89% higher than that by the state-of-the-art method of THAKARE method. For the abnormal event classification, the average accuracy of 74.06% is achieved, which is increased by 4.39% compared with the existing ZHOU method. The proposed method can detect and locate video anomalous events more effectively with better anomalous event classification performance.

To investigate the stress distribution and safety reliability of the baffle exposed to high temperature in heater, the stress of baffle was analyzed based on the finite element method. The dangerous section path was selected for the stress linearization analysis, and the structure of baffle was optimized according to the safety assessment results. The results show that the stress concentration caused by sharp angle has great influence on the stress distribution of   baffle, and the excessive film stress of PL is the main reason of stress assessment risk. The overall stress of baffle is increased with the increasing of fuel inlet velocity, and the temperature is positively correlated with the thermal stress. The stress change trend is similar at different speeds, and the structure discontinuity at the connection of heat exchange tube bundle and folding plate can cause stress increase. In the optimization scheme, the maximum thermal stress of baffle is reduced by 16.7% when the sharp angle is passivated. The maximum thermal stress with thermal barrier coating is decreased by 44.5% as compared with that for passive tip angle, and decreased by 53.7% as compared with that for the original structure. The overall thermal stress is decreased, and the strength of the optimized baffle plate is assessed as qualified.

To explore the characteristics of traffic volume data and improve the accuracy of parameter estimation for traffic volume data fitting, the parameter estimation method for traffic volume data fitting was proposed based on the maximum correlation entropy criterion. The traffic volume data was preprocessed, and the probability distribution of the data was specified. The maximum correlation entropy criterion was used to estimate the model parameters, and the gradient ascending method was used to output the model parameters. The estimated values of the probability distribution parameters were obtained, and the results were compared with those of the traditional methods. Based on the measured data and the four assumed distribution models of Normal distribution，Logarithmic normal distribution，Webull distribution and Rayleigh distribution, the performance evaluation was conducted.The results show that the proposed method has the best fitting performance, and the comprehensive evaluation indexes are respective 0.979 00, 0.726 08, 1.397 69 and 1.494 50, with strong accuracy and reliability.

To solve the adaptability problem of adaptive cruise control system for different styles of drivers, the personalized multi-mode adaptive cruise control algorithm was proposed. Based on the next generation simulation(NGSIM) data set, the car-following data were clustered and analyzed by K-means algorithm. The driver driving style was divided into radical, general and conservative types, and the corresponding car-following distance model was constructed. The gradient algorithm based on the deep deterministic policy was designed to modularize the basic performance of adaptive cruise, and the personalized car-following distance model was integrated into the basic performance of each mode. Based on the maximum entropy inverse reinforcement learning, the weights of personalization before the reward function were designed. To verify the multi-mode adaptive cruise control, the system was run in Simulink/Carsim joint simulation environment, and the results were compared with real vehicle samples of three driving styles. The results show that the tracking performance is good, and the inter distance and speed are close to those of the real samples of three types of drivers, which conforms to the driving habits of drivers and meets the personalized driving needs.

 To solve the problem of the existing underwater detection with low detection accuracy due to single variety and dense target, the improved YOLOv5 algorithm of RSC-YOLOv5 was proposed. The RepVGG Block module was used on Backbone to improve the recognition accuracy of targets of different scales and enhance the inference speed. The shuffle attention （SA）module was added to improve the feature extraction ability of the algorithm. Content-aware reassembly of features(CARAFE) upsampling was used in Neck to obtain larger receptive field. Varifocal Loss was introduced to pay more attention to the high-quality positive samples in intensive target sample training. The experimental results show that the average accuracy of the RSC-YOLOv5 fish and crab detection algorithm is 93.6%, which verifies that the proposed algorithm is suitable for the underwater fish and crab detection.

To investigate the influence of security measures on resident ride-hailing decision behavior and travel preference, the extended technology acceptance model was constructed. Based on the nationwide network questionnaire survey data, the multiple indicators and multiple causes （MIMIC）model was used to test the path of the extended technology acceptance model. The comprehensive weighted average method was used to evaluate the effectiveness of rectification measures. The results show that the security risk is the critical factor to prevent passengers from using ride-hailing. On the contrary, privacy risk has slightly negative impact on the use intention. The male, the group with high frequency of using ride-hailing and the group with old age have more positive attitude towards using ride-hailing again after safety rectification. The effectiveness analysis reveals that passengers have high recognition degree for safety response, driver reviewing and identification with scores of 6.43, 6.07 and 6.02，respectively.

According to the different braking strategies required by different braking conditions, the layered brake-by-wire control strategy was proposed. At the top layer, the residual collision time between the vehicle and the vehicle in front was calculated by the second-order TTC safety collision time model, which was used as basis for the selection of braking strategy. The vehicle two-degree freedom dynamic model, the car body normal force model and the Burckhardt tire model were established at the top layer. At the low level, the braking force was distributed among the wheels. By the sequential quadratic programming (SQP), the vehicle braking force was optimized by establishing an optimization function based on the tire slip rate under general braking, emergency braking and unstable braking conditions, respectively. MATLAB/Simulink and Carsim were used to carry out the joint simulation, and the braking distribution strategy under the three working conditions was verified respectively. The results show that the braking distance and braking time are reduced by respective 18.08% and 25.12% under normal braking conditions, while the braking distance and braking time are reduced by respective 19.17% and 12.79% under emergency braking conditions. Under unstable braking conditions, the proposed strategy can improve the lateral stability of the vehicle through tire differential twist. The proposed braking strategy can significantly improve the braking efficiency of the vehicle.

To generate orbital angular momentum vortex waves with different modes, the microstrip antenna was designed using graphene materials, and the antenna dimensions and materials were provided. The antenna was light in weight, simple in structure and easy to manufacture, which could generate orbital angular momentum vortex waves with mode numbers l of -2, 0 and +2 only by adjusting the feed phase difference. Through parameterized scanning, the antenna properties were evaluated in terms of impedance matching, return loss, operating bandwidth and gain, and the influence of graphene on antenna properties was analyzed. The simulation results show that when the chemical potential of graphene is 0.9 eV, the return loss of microstrip antenna at 14.90 GHz can reach -47.00 dB with the gain of 9.25 dB, and the performance is improved significantly compared with the traditional antenna. Adjusting the chemical potential of graphene can also improve the return loss of antenna.

To improve the anti-wear and anti-fatigue ability of raceway surface of the shield main bearing, SYSWELD software was used to carry out the numerical simulation of laser transformation hardening under different parameters for the shield main bearing raceway material of 42CrMo steel. The effects of laser power, scanning speed and spot size on the phase transformation of 42CrMo steel for the raceway material of shield main bearing and the depth of hardened layer were investigated. The results show that with the increasing of laser power, the decreasing of laser scanning speed or the decreasing of laser spot size，the depth of raceway hardened layer is increased. When the laser power is 2 250 W with scanning speed of 20 mm/s and spot length of 5 mm, the depth of the hardened layer of the raceway can reach 1.08 mm, and the hardened layer is relatively uniform. 

 When the vehicle works normally, the internal parts of transmission are often damaged due to high temperature caused by shifting and other behaviors, which affects the normal driving of the vehicle. Therefore, it is necessary to study the transient temperature field of the transmission. According to the thermal load characteristics of transmission components, the transient thermal network model of transmission was established by thermal network method in the forms of heat source and thermal resistance. The temperature of each transient node in the transmission was calculated, and the temperature rise of high-heat parts of wet clutch and planetary wheel during the whole process of vehicle starting to driving smoothly was analyzed. The results show that the temperature rise law of wet clutch is related to the gear shift. The temperature rise law of the gear is changed with the change of shift position. Temperature rise of parts in planetary gear is basically the same and changes steadily.

 For the system log data with the distribution characteristics of "group anomaly" and "local anomaly", traditional semi-supervised log anomaly detection method of anomaly detection with partially observed anomalies(ADOA) has poor accuracy of pseudo-labels generated for unlabeled data. To solve the problem, the improved semi-supervised log anomaly detection model was proposed. The known abnormal samples were clustered by k-means, and the reconstruction errors of unlabeled samples were calculated by kernel principal component analysis. The comprehensive anomaly score of sample was calculated from reconstruction error and similarity to abnormal samples, which was used as pseudo-label. Sample weights for the LightGBM classifier were calculated based on pseudo-labels to train the anomaly detection model. The impact of the proportion of training set samples on model performance was explored through parameter experiments. The experiments were conducted on two public datasets of HDFS and BGL. The results show that the proposed model can improve the pseudo-label accuracy. Compared to existing models of DeepLog, LogAnomaly, LogCluster, PCA and PLELog, the precision and F1 score are improved. Compared to traditional ADOA anomaly detection methods, F1 scores are increased by 8.4% and 8.5% on the two datasets, respectively.

Numerical and experimental studies were performed on the flow and heat transfer characteristics of impingement-film composite cooling structures on center cone. The results show that the gas recirculation occurs on the surface of center cone, and the recirculation vortex is generated. The vortex scale is increased with the increasing of blowing ratio. The hot wall temperature of center cone is increased along the flow direction, and the increasing of blowing ratio can improve the overall cooling effectiveness and increase the relative pressure drop. Increasing film-hole diameter can improve the overall cooling effectiveness and reduce the relative pressure drop. With the increasing of impinging-hole diameter, the overall cooling effectiveness is decreased slightly， and the relative pressure drop is decreased to some degree. The variation of impinging distance has little influence on the flow and heat transfer law.

To reduce the temperature of heating wall and improve the uniformity of temperature distribution, the flow boiling heat transfer process of HFE-7100 in the wavy microchannel was numerically simulated, and the effects of microchannel structure and densification methods on the flow boiling characteristics were discussed. The results show that when the waviness throughout the whole flow direction is densified with increasing the ratio of wave amplitude to wave pitch from 0.04 to 0.08, the equivalent diameter of bubble at the outlet of microchannel is decreased to some extent，and the segmental annular flow is also suppressed. However, the pressure loss between the outlet and inlet of microchannel is increased significantly with the maximum increasing up to 140.54%. Increasing the waviness of the upstream, midstream or downstream in the microchannel can effectively inhibit the segmental annular flow from occurring at the outlet section and reduce the heating wall temperature for improving the temperature distribution uniformity of heating wall. The comparison illuminates that increasing the downstream waviness can more significantly reduce the bubble growth rate. The heating wall temperature and distribution can be effectively controlled as well, which is equivalent to that of the whole densified microchannel. However, the pressure loss between the outlet and inlet can be reduced by 34.23%. Increasing the downstream waviness in the wavy microchannel is  preferable method for improving the flow boiling heat transfer characteristics.

The direct data-driven control problem of an unknown class of Lorenz-type chaotic systems was investigated. The systems were transformed into linearly dependent form to construct the non-parameterized model. The state feedback controller was designed for the reconstructed system. The input and output data of the system were sampled offline, and the collected data were directly used to design and give the expression of the state feedback controller. The stabilization problem of the system under state feedback control was verified,and the stability of the closed-loop system was proved. Through numerical example simulation verification, the results show that the proposed direct data-driven control strategy is effective.

Steel slag was used to replace some basalt coarse aggregates in porous asphalt mixtures, and the laboratory tests and numerical simulations were conducted to investigate the fatigue performance of steel slag porous asphalt mixtures. Based on the CT scanning image, the steel slag, basalt, asphalt mortar and pores in the mixture were differentiated by threshold segmentation and imported into particle flow code（PFC） to establish discrete element model of steel slag porous asphalt mixture with particle diameter of 0.4 mm. Based on the macro-meso mechanical parameter conversion and parameter correction through virtual strength test, the virtual fatigue damage numerical simulation was carried out to obtain the specimen microcrack propagation path and fatigue life under repeated load. The results show that the virtual strength test can well fit the peak load and displacement curve of steel slag porous asphalt mixture specimens. The crack propagation path of the specimens conforms to the actual fracture situation, and the distribution of cracks mainly propagates along the internal pores of the model. The fatigue damage model can depicte the trend that the parallel bond diameter is decreased with the increasing of load cycle and decreased acceleratingly with the increasing of tensile stress between parallel bond contacts. The results of the virtual fatigue damage model are relatively close to those of laboratory fatigue damage test with an error of less than 10%, indicating that it can effectively predict the fatigue life.

To provide more reliable safety recommendations for vehicles navigating curves, the lane curvature measurement method based on binocular stereo vision was investigated. The binocular camera was calibrated by the MATLAB camera calibration toolbox to obtain intrinsic and extrinsic parameters. The original left and right images from the binocular camera were input into the stereo matching network to generate disparity map. The Sobel edge detection and threshold detection were applied to the region of interest (ROI) in the original left camera image, and the results were fused. The perspective transformation was applied to the fused results within the ROI to obtain binary image. The positions and quantities of white pixels within the ROI were statistically analyzed by the histogram. The lane lines were fitted by the least squares method, and the fitting accuracy was enhanced by Kalman filter prediction algorithm. The pixel coordinates of the lane lines were determined based on the inverse affine transformation and the fitting results. The disparity values within the disparity map were obtained according to the pixel coordinates, and the three-dimensional coordinates of the lane lines relative to the main camera of the binocular system were derived by triangulation. The distance conversion scale between the measured real-world coordinates and the image pixel coordinates was calculated and applied to the lane curvature computation. The results show that by the proposed algorithm integrating Kalman filter prediction for lane line detection and binocular stereo vision for lane curvature radius measurement, the average deviation of 5.98% in the calculated curvature radius of curves is achieved, which can ensure the robustness of lane line fitting for enhancing the accuracy and reliability of the binocular stereo vision-based lane curvature measurement method.

To improve the work efficiency and screening yield in the circular crushing process, taking tomato straw as example, the screening performance of the spiral screening device was investigated. The numerical simulation and experimental research by EDEM software were conducted to analyze the effects of spiral speeds and charging speeds on the screening performance of spiral. The results show that with the increasing of spiral speed, the screening rate is increased with latter decreasing, and the output is gradually increased with latter unchanging. With the increasing of charging speed, the screening rate  is gradually decreased, and the output is gradually increased. Increasing the spiral speed can improve the sieving flow condition of crushed straw  particles and reduce the sieving time of unit straw  particles. The high sieving rate of 37.36% and high yield of 139.89  kg/h can be obtained when the spiral speed is 180  r/min. Increasing the charging speed can obstruct the flow of raw materials in the sieving process, which makes the sieving rate decrease. The high material concentration brings high yield, while the yield growth rate is gradually decreased. The inlet mass flow rate of 0.05 kg/s can control the screening rate more than 35% with high output, and the simulation results are verified by the experiments.

To solve the problems of poor stability and driving safety of the ego-vehicle caused by the variability of the preceding vehicle′s driving conditions, taking the hub-motor-driven electric vehicles as research object, the constant speed/follow-up cruise switching strategy was proposed with considering the inter-vehicle distance and relative speed simultaneously. The optimized safe time-headway model was designed, and the longitudinal tracking control strategy for the automotive adaptive cruise control (ACC) system was developed based on hierarchical control. The upper-level controller based on model predictive control (MPC) was established to solve the desired acceleration of vehicle. Utilizing the inverse longitudinal dynamics model, the lower-level controller was constructed to solve the output torque. The torque distribution strategy for the front and rear axles was designed, and by the electric motors, the wheel drive and braking torque were precisely controlled to achieve longitudinal tracking control for ACC. The co-simulation model was established by Matlab-Simulink and Carsim software to conduct simulation tests under three driving conditions of variable speed, variable acceleration and variable deceleration. The simulation results demonstrate that the proposed adaptive cruise control system based on hierarchical control can stably follow the preceding vehicle′s movement under different driving conditions with maintaining safe inter-vehicle distance, which exhibits excellent following stability and safety.