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 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.

 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.

 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 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. 

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.

To solve the problems of boundary ambiguity and difficult nested entity extraction in nested named entity recognition (NER), the nested named entity recognition method based on multiplexed local feature integration was proposed. By the proposed method, the sequential features were decomposed into forward and backward representations by the bidirectional long short-term memory network. The nested NER task was segmented by the entity length, and the fixed-length local features were integrated by the multi-scale convolutional networks. The forward and backward features were matched to generate prediction results. The pre-weighting mechanism was introduced to mitigate the inter-layer information transmission errors in deep architectures. The experiments were conducted on the ACE2005 and GENIA datasets for comparing the proposed method with the existing nested NER approaches. The results show that compared with the best Dependency Parsing method of other methods, the proposed model achieves superior performance with improved F1 scores by 0.18% and 0.03% on ACE2005 and GENIA, respectively. By the proposed method, a certain performance improvement can be achieved compared with the current state-of-the-art models.

To solve the problem of excessive time spending on modeling, calculation and simulation optimization in conventional optical structure design, the novel optimization method combining convolutional neural networks and genetic algorithms for narrow linewidth spectral structure parameters was proposed. Using the Y-shaped all-dielectric metasurface structure as model, 4 096 datasets were generated by finite difference time domain (FDTD) simulation to train the forward prediction network. The trained network was further combined with the genetic algorithm to optimize the parameters of metasurface structure. The simulation results show that the loss value of the trained prediction network on the test set is only 5.6×10-4. Compared to the original dataset, the minimum full width at half maximum (FWHM) obtained by combining the optimization algorithm is reduced by 0.040 nm. Compared with the traditional methods, the proposed method can enhance the optimization efficiency and effectiveness of complex metasurface structures.

To improve the operating efficiency of hysteresis motor, the low harmonic design using unequal turns concentric winding (UTCW) was proposed. The structure and operating principle of hysteresis motor were discussed, and the air-gap magnetic field was analyzed based on the magnetic field modulation theory, which revealed that the high order harmonics of air-gap magnetic field were the fundamental reason for reducing the operation efficiency and torque density of hysteresis motor. The design of UTCW was theoretically derived, and the low harmonic winding design scheme for 18/2 pole hysteresis motor was obtained. The finite element model of hysteresis motor was established, and the performance of hysteresis motor was calculated. A hysteresis motor prototype was manufactured and tested. The results show that compared with the traditional distributed winding motor, the UTCW motor performance can be improved effectively with the motor torque increased by 11.4%, the torque ripple reduced by 5.5% and the operating efficiency improved by 9%. 

 To solve the problems of the low accuracy of voltage vectors output from conventional single-vector control and the high switching frequency of conventional dual-vector control, the dual-vector model predictive control algorithm with capability of selecting output voltage vector order was proposed. By calculating the duty cycle ratio and comparing the voltage vectors before and after for selecting the output voltage order, the switching signals of the output voltage were reordered to reduce the switching frequency of the dual-vector algorithm. MATLAB/Simulink was used to establish the simulation model to verify the proposed strategy. The test was completed on the platform based on RTU-BOX204 controller. The results show that the new algorithm has better steady-state response compared with the traditional single-vector control and lower average switching frequency compared with the dual-vector control, while the switching loss is reduced.

To solve the speed fluctuation caused by the rotor misalignment fault in permanent magnet synchronous motor（PMSM）system, the resonance active disturbance rejection control (RADRC) method was proposed with integrating sinusoidal disturbance model and resonant extended state observer. The mathematical model of rotor misalignment fault system was used to analyze the fault mode of motor under sinusoidal disturbance and establish the electromechanical coupling model under fault state. The PI controller used in the speed loop in the control system was replaced by the active disturbance rejection controller to improve the response speed and steady-state performance. The feed forward compensation was utilized to reduce the impact of misalignment faults on system performance according to the optimized design of resonance extended state observers by analyzing sinusoidal disturbances. The simulation and experiment platforms of PMSM misalignment fault were established, and based on the proposed RADRC and traditional ADRC, the speed regulation performance comparison and verification were carried out. The results show that when the motor speed fluctuates under the interference of misalignment faults, the RADRC method can rapidly track the given speed with slight overshoot and effectively suppress the fluctuation of the speed. When the control quantities are the same, by the proposed method, the rotational speed fluctuation can be suppressed more rapidly and effectively, and the operating performance of PMSM system under misalignment faults is improved.

To investigate the influence of joint on concrete durability under coastal environment, three concrete specimens with direct wet joints, chiseled wet joints and epoxy glue joints were designed and casted. Different levels of pre-pressure load were applied to the specimens after curing, and the chloride ion erosion test was conducted after unloading, which were used to investigate the effects of joint type, pre-pressure load level and replacement ratio of cement capillary/crystalline waterproofing materials (CCCWM) on concrete resistance to chloride ion. The results show that except for the epoxy glue joint, the chloride ion concentration at concrete joint is significantly greater than those at both sides of joint in the concrete specimens of the other two types of joints. The greater the distance from the joint is, the smaller the chloride ion concentration in concrete is. Compared with the pre-pressure load level of 0.4fc（fc is the ultimate compressive strength）, the chloride ion transport rates in the concrete at the joint and on both sides of the joint are significantly accelerated at 0.8fc load level. The doping of CCCWM can improve the chloride ion erosion resistance of concrete joint. With the increasing of CCCWM replacement, the chloride ion corrosion resistance of concrete joint is gradually increased.

Based on the principles of bionic design inspired by human bone joints, the prefabricated steel-concrete composite joint with artificial plastic hinge was proposed. The numerical analysis model of nine joint configurations was developed by ABAQUS. Applying low-cycle reciprocating loads on the beam ends, the effects of axial compression ratio, flange connection plate thickness and shear energy dissipation bar diameter on the key seismic performance indicators of load-bearing capacity, plastic deformation, performance degradation and energy dissipation were investigated. The results show that under various parameter conditions, the hysteretic curves of the artificial plastic hinge joint exhibit full and stable loops with strong energy dissipation capacity and minimal performance degradation. The axial compression ratio and shear energy dissipation bar diameter have limited impact on the load-bearing capacity, plastic deformation and energy dissipation capacity, while they can affect the initial stiffness to some extent. The flange connection plate thickness significantly affects the load-bearing capacity of the joint, but its impact on initial stiffness can be negligible. Excessive axial compression ratio and flange connection plate thickness intensify the stress concentration in the joint core area, while increasing the diameter of the shear energy-dissipating rod reduces the stress concentration in the joint core area.

To investigate the impact of A-site disorder on the electronic and magnetic properties of double-layer perovskite oxide,the atomic arrangement, magnetic configuration and electronic structure of double-layer perovskite oxide SrCaNiTeO6 were systematically investigated by first-principles calculation. The results show that the compound has antiferromagnetic semiconductor behavior with B-site Ni antiferromagnetic coupling among the three different atomic arrangement structures constructed by considering all possible arrangements of Sr and Ca atoms at A-site, and B′-site Te is nonmagnetic without contribution to the cell magnetic moment. Electronic structure analysis confirms that the charge combination of SrCaNiTeO6 is Sr2+Ca2+Ni2+Te6+O2-6. Although the A-site ion between Sr and Ca is disordered,the structure maintains the original space group of P21/n with unchanged electronic structure.

To explore the best sponge regulation mode for coastal reclamation area under the condition of large-scale excessive rainstorm, taking the Block C5-1 in Taizhou City, Zhejiang Province as example, the storm water management model (SWMM) was used to simulate the effects of three sponge regulation design schemes on the control of water logging in Block C5-1 with source reduction, source reduction+process control and source reduction+process control+end storage. The results show that after source reduction regulation, the reduction rates of overflow nodes number, total overflow time and total overflow amount are 4.50%, 12.38% and 15.00%, respectively. After source reduction and process control regulation, the reduction rates of the three parameters are 39.38%, 86.94% and 69.50%, respectively. After source reduction, process control and end storage regulation, the reduction rates of the three parameters are 59.36%, 93.30% and 87.50%, respectively. The measures of increasing interception multiple and adding flood discharge channels in process control have the most significant effect on reducing rainwater and flood. The combination of source reduction, process control and end regulation has the best effect on controlling rainwater runoff and node overflow in coastal reclamation areas.