The traditional deadbeat predictive current control is dependent on mathematical model. The parameter mismatch caused by temperature change will increase the current response error and current pulsation. To solve these problems， a deadbeat predictive voltage compensation control （DPVCC） strategy based on composite sliding mode disturbance observer （CSMDO） was proposed. The observer can estimate the future current value and the lumped perturbation of the system simultaneously and compensate them into the voltage vector to improve the robustness. The MATLAB/Simulink simulation and experimental results based on a 30 kW permanent magnet synchronous hub motor show that DPVCC can reduce the current error more than 60%， which can restrain current pulsation， improve the accuracy and robustness of the control system.

In consideration of the terrible real-time performance and adaptability in vehicle stability control for distributed electric vehicles， a model reference adaptive control strategy based on hierarchical control structure was proposed in this paper. The upper controller was a model reference adaptive yaw moment controller based on Popov hyperstability. The adaptive feedforward and feedback gain was calculated by solving the Popov integral inequality. It had the advantages of small computational burden， excellent self-adaptability， and ensured hyperstability of the controller. The lower controller distributed differential wheel torques according to the desired yaw moment via quadratic programming. Actually， The distribution of wheel torques was exactly the solution of a weighted minimum square problem， which was solved by the active-set method. The proposed hierarchical stability control strategy was verified by the Carsim/Simulink co-simulation platform under the condition of fish hook in a low road adhesion coefficient. The simulation results show that the peaks of yaw rate and sideslip angle decrease 44.81%， 88.68%， respectively， compared with no control. The peaks of yaw rate and sideslip angle decrease 30.48%， 55.72%， respectively， compared with PID control. The proposed stability control strategy can significantly improve vehicle handling and stability performance， and increase stability of vehicles under extreme conditions.

Needle dynamic characteristics and the influence of control current on needle motion about gasoline direct injection （GDI） nozzle were researched based on ultrafast X-ray imaging technology. Transient characteristics of in-nozzle flow and near-field spray jets during needle lift process and effects of cavitation were simulated based on a standard 8-hole GDI nozzle. A real needle motion profile obtained by ultrafast X-ray imaging technology was applied to reflect the influence of needle lateral vibration on hole-to-hole non-uniformity. The experimental results show that vibration occurs when needle module collides with iron core in the opening stage and with needle seat in the closing stage， which makes it difficult to control fuel injection precisely. Reduction of control current contributes to less vibration and more precise fuel injection. The simulation results show that cavitation at the upper face of nozzle leads to hydraulic flip phenomenon， so the fuel jet deviates from nozzle axis. Such kind of jet deflection is enhanced by intense entrainment between the jet and the ambient gas， and the maximum deflection angle is up to 5°. The jet deflection will cause nozzle tip wetting at the lower boundary of the contour-bore surface， which will deteriorate the engine particle emissions performance. The study also indicates that the lateral vibration of needle results in non-uniform flow characteristics between different holes， and such non-uniformity decreases as the needle lifts higher.

Aiming at the problem that a certain type of claw-pole generator has obvious order aerodynamic noise characteristics under high-speed conditions， a noise test experiment is designed to obtain the main order noise， and the finite element software is used to establish a numerical simulation model of the flow field and sound field. The pressure distribution of the front and rear fan blades， the pressure and sound power distribution of the stator and rotor are analyzed， and the influence of different parts on the aerodynamic noise of the claw-pole generator is studied. The fan blade distribution angle was optimized by the vector synthesis method， and the simulation and test are established for the optimized fan blade to verify the aerodynamic noise optimization effect of the claw-pole generator. Experimental results show that the average noise reduction of the 8th and 12th order of the optimized claw pole generator in the entire speed range has reached 2.5 dB.

Aiming at the problem of heating and reliability of traditional energy recovery system， a potential energy interactive recovery system of excavator boom based on multiple hydraulic cylinders was designed， and its characteristics were researched. The working principle and energy characteristics of the boom potential energy interactive recycling system were analyzed. According to the operation characteristics of the 35 t excavator and the change of boom potential energy， the system parameters were designed based on the hydraulic cylinder and accumulator. Furthermore， the virtual simulation model of excavator energy recovery was established to analyze the operation characteristics and energy-saving efficiency of the system. Finally， an energy recovery system test platform was built to test the feasibility and energy efficiency characteristics of the system. The simulation and test results of single action of boom lifting show that， compared with the conventional excavator， the peak pressure of the main pump output of the energy recovery system is reduced by 57.8%， the energy-saving efficiency of the system is 51.5%， while the controllability of excavator remains good at the same time.

According to the advantages and disadvantages of hydrostatic transmission mode and mechanical transmission mode， and the characteristics of power split mechanism and power confluence mechanism， the configuration analysis was carried out， and 5 typical hydro-mechanical composite transmission schemes was put forward. The structures of each transmission scheme were introduced according to mechanism kinematics， and the characteristics of different working modes were analyzed by combined with speed regulation characteristic curves of each transmission. Taking a typical transmission device as the research object， the simulation model based on Simulation X was built， the engaging sequence of clutches was set up， the speed changing curves of hydraulic system， confluence mechanism and output shaft in different gears were analyzed. The results show that， the actual speed regulation curves of variable speed transmission device are consistent with the theoretical speed regulation curves basically， a good continuous speed regulation function in a large range is realized， and the related research results of hydro-mechanical composite transmission can provide theoretical reference for the practical engineering application.

Considering that the simplified processing in the process of establishing the dynamic model of the spindle system will lead to a large error， a dynamic modeling method based on response surface and genetic algorithm is proposed. Firstly， the dynamic model of the spindle system is established by considering the stiffness of the spindle-holder joint surface and the bearing support stiffness. Secondly， the main factors that affect the accuracy of dynamics analysis are developed by comparing the first three natural frequencies of the spindle system model with rigid and flexible connections. Then， sample points of contact stiffness are constructed based on the dynamics model of the spindle system， and the stiffness of the spindle-holder joint in the sample point is used as the input parameter， then the first， second， and third order natural frequency are used as the output parameters， and the non-parametric regression method is used to fit the response surface. The relative value of the determination coefficient and the root mean square error is used to test the accuracy of the response surface. The multi-objective genetic algorithm is used to update the stiffness of spindle-holder joint， which is used to update the dynamic model of the spindle system. Finally， a case study shows that the proposed method based on the fusion response surface methodology and optimization algorithm has higher analysis accuracy.

As for the fault diagnosis of rolling bearings for urban rail trains，an integrated intelligent diagnosis method was proposed based on principal component analysis （PCA） and random forest （RF）. Firstly， original feature vectors of 21 dimensions were extracted from time domain and frequency domain. Principal component analysis was then utilized to conduct dimension reduction accounting for the correlations within features. Finally， the size-reduced features act as the inputs of the RF. Consequently， an integrated intelligent diagnostic model called PCA-RF model was established. Experimental results demonstrate that the PCA-RF model is superior to other methods such as support vector machines and RF with original feature vectors as inputs， in terms of efficiency and effectiveness. In addition， the proposed method can obtain satisfied identifications among complicated bearing fault patterns which involve not only various fault locations but fault severity levels. In short， the PCA-RF model had a certain value in the diagnosis of rolling bearing faults of urban rail vehicles.

Based on the perfomance of small volume and high heat exchange efficiency for the microchannel heat exchanger and widely use in the fields of microelectronics， industrial refrigeration， electric vehicle air conditioning and so on， the physical model and mathematical model of microchannel low temperature heat exchanger are established and solved in the paper. To reveal the flow and heat transfer performance of microchannel low-temperature heat exchanger with hydraulic diameter less than 1 mm， the effects of changing inlet velocity and different aspect ratio on the resistance pressure drop， wall temperature， channel outlet fluid temperature and friction coefficient of the heat exchanger are studied. The results show that the above parameters were obviously effected by different aspect ratios. The microchannel pressure drop increases with the increase of flow velocity， and the increasing trend of pressure drop increases. The wall temperature， channel fluid outlet temperature and friction coefficient decrease with the increase of flow velocity， and finally tend to be stable. The parameter values of the channel with aspect ratio of 4 are generally larger than those of the channel with aspect ratio of 1. The aspect ratio has a great impact on the heat transfer performance of the microchannel heat exchanger.

The interlayer toughened basalt fiber/unsaturated polyester resin （BF/UP） composites with short basalt fiber（SBF） were prepared. The Mode II interlaminar fracture toughness， the tensile properties and bending properties of composites with different lengths and surface densities were investigated. The studies show that SBF can improve the Mode II interlaminar fracture toughness， tensile properties and bending properties of composites. When the length of SBF is 6 mm and the surface density is 30 g/m²， the Mode II interlaminar fracture toughness， tensile strength， elongation at break， flexural strength and energy absorption of the composites have the maximum improvement， which are 103%， 27%， 50%， 101% and 58% respectively.

In order to study the friction and wear characteristics of simulated lunar dust and typical metal materials， three kinds of simulated lunar dust， JLU-D1， JLU-D2 and JLU-D3， were prepared from volcanic ash in Jinchuan Town， Huinan County， Jilin Province. Three metals， stainless steel， aluminum alloy and copper alloy， were selected as the samples to be worn. The friction and wear tests were carried out using the UMT tester. The wear morphology， friction coefficient， wear scar depth and wear amount of the three materials after simulated lunar dust friction were analyzed. The results show that the maximum wear depth difference of aluminum alloy， copper alloy， and stainless steel is 74.05 μm， 27.73 μm， and 15.25 μm， respectively. The friction coefficients of aluminum alloy， copper alloy， and stainless steel are （0.2262~0.2835）， （0.1545~0.1855）， and （0.4198~0.4958）， respectively. The wear of aluminum alloy， copper alloy， and stainless steel increases by 50%， 28.6%， and 97%， respectively. The results show that aluminum alloy， copper alloy， and stainless steel are the most severely worn materials by lunar dust. The initial friction coefficient has no obvious correlation with the strength and hardness of metal materials. The friction coefficient of stainless steel is the largest， and that of aluminum is greater than that of copper. The friction and wear of small particle size lunar dust on the typical metal surface is more serious than that of large particle size lunar dust.

In the case of ice and snow weather， the signal control efficiency of the existing signal scheme is substantially reducing， which further leads to the traffic congestion. So starting from the operation characteristics of traffic flow， we analyzed the variation of traffic flow parameters under different snowfall amounts. Using regression fitting， the existing Merriam-Webster delay calculation formula is corrected. Taking the intersection of Jiefang Avenue and Renmin Avenue in Changchun City as an example， Vissim software is used for simulation analysis. The results show that in the ice and snow weather， compared with the signal timing scheme under normal weather conditions， adjusting the signal timing scheme according to the snowfall can effectively reduce the average delay of vehicles at the intersection and improve the traffic efficiency of the intersection. These analysis results are also suitable for the optimal control of traffic signals in all snow and ice weather environments.

For the detection and repair of anomaly in expressway toll data， an anomaly detection algorithm based on SSC （Sum of Similar Coefficients） suitable for multi-dimensional reading data joint detection and an anomaly repair algorithm based on XGBoost （eXtreme Gradient Boosting） multidimensional data prediction repair method are proposed，and above methods are applied to data test. The results show that the SSC considers the correlation between data dimensions and could accurately detect anomaly in multi-dimensional data. Meanwhile， compared to the improved Lagrange interpolation， the R² of the proposed method increased from 0.9166 to 0.9856. The algorithms proposed in this paper are effective and could provide high quality data support for data analysis and statistics of expressway management departments.

Based on the real-time road network traffic information， a joint path planning model of electric vehicle charging and battery swapping， which fully considers the driving time， charging stations and detour index was presented in this paper. In order to overcome the problem that the traditional A^* algorithm can not correct the path in dynamic road network， an adaptive A^* algorithm based on hierarchical programming is proposed. The case simulation shows that the navigation strategy proposed in this paper can select the optimal charging station， carry out the corresponding path planning， and reasonably balance the number of electric vehicles at each charging station， which not only shortens the overall driving time， alleviates the congestion caused by the gathering of vehicles near the charging station， but also improves the operation economy of the charging station. The improved algorithm constructs a index of layered road network， which can solve the model according to the real-time information of road network， effectively improve the search efficiency and reduce the calculation time.

In order to clarify the humidity status of the typical cracks in the asphalt pavement during the initiation and development， the humidity field simulation results of the top-down cracks and reflection cracks of the asphalt pavement was carried out， and the variation law of depth and water vapor concentration at top-down cracks and reflection cracks was clarified. The results show that the double sine function model established can better describe the change of relative humidity of atmosphere. The moisture concentration at the top-down cracks is lower than that of the normal pavement， and the moisture concentration at the reflection cracks is higher than that on the normal pavement. This result can be used as a theoretical basis for the research on the initiation and development of cracks under actual humidity and the rational evaluation of the service performance of asphalt pavements.

According to the actual of emulsified asphalt cold recycled mixture pavement in seasonal frozen area， the freeze-thaw cycle test is designed to explore the performance decay law of the unsaturated emulsified asphalt cold recycled mixture under freeze-thaw cycles. Through the emulsified asphalt cold recycled mixture with different number of unsaturated freeze-thaw cycle， emulsified asphalt cold recycled mixture of ultrasonic wave velocity， void fraction， freeze-thaw splitting strength， low temperature splitting strength and high temperature shear strength were determined， emulsified asphalt cold recycled mixture macro performance changes are analyzed. The micro morphology of the mixture under different times of unsaturated freeze-thawing cycle is observed by Scanning Electron Microscope （SEM）. The results showed that after 20 cycles of unsaturated freeze-thaw， the void ratio of the emulsified asphalt cold-recycled mixture increased by 1.06%， the ultrasonic wave speed decreased and the splitting strength decreased by 26.3%. Both high temperature performance and low temperature performance are greatly damaged. The cement hydration products at the interface between emulsified asphalt and cement mortar in the mixture structure are damaged from the outside to the inside by freeze-thaw cycles， the asphalt mortar peels off， the adhesion between asphalt and aggregate decreases， and the space structure between aggregates is gradually damaged， which eventually leads to the performance damage of unsaturated emulsified asphalt cold recycled mixture.

In order to avoid bridge safety accidents caused by bearing capacity， a bearing capacity detection method of long-span concrete-filled steel tube arch bridge is proposed based on gray theory. The sensor is used as the main tool for bearing capacity detection， the detection system architecture is determined， and the control center， data transmission， acquisition and other functional modules are designed. Communication protocols are added， and sensitivity is set in combination with the sensor motion equation to ensure real-time data transmission. The mean value method is used to generate the grey sequence of the test data， the degree of cracks is taken as the main factor affecting the bearing capacity， and the grey correlation analysis and the transformation of the initial matrix are used to obtain the correlation coefficient. Standardize the standard deviation， standardize the test sequence， and obtain the parameters to be tested through accumulation and mean value processing to complete the bearing capacity test. The simulation experiment shows that the proposed detection method has good dynamic response performance. The detection results in both static and dynamic test environments show that there are no serious cracks in the bridge， and the bearing capacity is close to the theoretical bearing capacity.

Based on the new building structure system with external shear and internal frame， a method of connecting prefabricated concrete shear walls joints with ultra-high performance concrete （UHPC） is proposed. In order to explore the seismic performance of shear walls under this connection mode， low cyclic tests were carried out on one ordinary cast-in-place concrete shear wall and two fabricated concrete shear walls connected by UHPC.Test results show that：The failure law of shear wall specimens under the two connection modes is the same， which is flexural failure；Under the same axial compression ratio， the ultimate flexural capacity of prefabricated concrete shear walls connected by UHPC in 10d is only 5.61% lower than that of ordinary cast-in-place concrete shear walls；UHPC can ensure the strength of assembled concrete shear wall connection section， and the prefabricated shear walls under UHPC connection have good seismic performance and good ductility. During the whole loading process， the three shear wall specimens show similar stiffness degradation and energy consumption change rules. On the basis of the experiment， the finite element software ABAQUS simulation shows that the finite element simulation results are in good agreement with the experimental results. When the lap length of steel bars reaches 16d， the bearing capacity， stiffness and the lifting amount of corner are in the best condition.

The cable-stayed system reinforcement method has been applied to some projects in recent years. The joints under the bridge have the characteristics of bearing combined shear and compression loads， double-shear interface， bonded steel plate and anchored bolt mixed connection， etc. The mechanical properties and failure modes are not clear. An accurate finite element model of a connector under the bridge was established by Abaqus finite element method， and its mechanical properties and failure modes were studied. The results show that the final failure mode of each model is the bolt shear failure. The final failure position of the model is divided into two categories： the bolt shear failure at the glue layer and the bolt shear failure at the contact surface of the two steel plates. Under the load， there are 4 kinds of bearing capacity change trends of the connectors under the bridge， and the bearing capacity of some models decreases obviously before failure. When only a single element among the factors that affect the ultimate bearing capacity of the model changes， the ultimate bearing capacity doesn't completely change in a monotonous trend， there is a complex coupling relationship between every element.

For the disadvantage that the number of resources in digital communities is huge and it is difficult for users to find the required data easily and quickly at the first time, a dynamic recommendation method for virtual community knowledge was proposed based on cyclic knowledge graphs. The knowledge point neighborhood entities in the virtual community were treated as contexts to obtain knowledge expression learning entities. The cyclic knowledge graph was fused with the data to be recommended, the user's historical click information was calculated, and the entity feature vector was extracted. Meanwhile, the user model in the virtual community with several neural co?filtering layers was established, implicitly interacting users and knowledge point relationships. The implicit vector of users to be recommended and the implicit vector of knowledge points were nonlinearly transformed several times to complete dynamic recommendation. The experiments prove that the recommendation satisfaction is high, and the recommendation results are comprehensive and not homogenized.

The traditional advGAN method can efficiently generate adversarial samples with high fidelity， but advGAN tends to overfit the original sample spatial manifold， resulting in poor transferability. To address this defect， an approach based on ensemble generative adversarial networks was proposed. Generating adversarial examples with high fidelity and high attack success rate in real-time is available according to previous approach based on generative adversarial networks， however it lacks the adversarial transferability. For generating transferable adversarial examples in real-time， an ensemble training strategywas proposed for adversarial transferability improvement. By using the expectation of an ensemble of substitute models， the generative network generates adversarial examples with better transferability and still holds good fidelity. The experiment result shows that the proposed approach： On MNIST datasets， the transferable attack success rate increases by 6% on average， up to 43.9%； On CIFAR-10 datasets， the transferable attack success rate increases by 7.6% on average， up to 75.62%；The PSNR increases slightly on both datasets. The experimental evidence indicates that the proposed ensemble advGAN method generates adversarial examples with higher transferability and fidelity in real time comparing with normal advGAN.

In order to solve the problems of gray information loss and poor image effect caused by edge blur of digital image， a digital image fuzzy enhancement algorithm based on convolution neural network is proposed. At the same time， restricted contrast balanced derivative image， gamma conversion derivative image， logarithmic conversion derivative image and two-channel enhancement derivative image are constructed under different lighting environments， so as to slightly adjust the variance of image pixels and enhance the expression of detail information. Finally， the convolution neural network is used to filter the transition points in the image， so as to adjust the contrast， average brightness and pixel variance in the image， and the fuzzy enhancement of the digital image was realized. Experimental results show that the proposed method can enhance the effect of digital image blur better， and is not easy to be affected by the environment.

For the problems of false warning， low detection efficiency and insatiable lightweight deployment of railway foreign object intrusion algorithms based on computer vision， a method of detection foreign object intrusion in railway region of interest based on lightweight network is proposed.. Firstly， the railway track line is detected by perspective transformation and cubic function fitting. By finding the area where the rail is located， and then expanding the division of dangerous area and safe area， the region of interest of railway foreign object intrusion detection is obtained. Secondly， a lightweight railway foreign object detection model is constructed by using sparse and channel pruning methods to compress YOLOv3 model. Finally， through the railway data set and field test， it shows that proposed method has high detection accuracy and detection speed. The lightweight model parameter space is reduced to 1/5 of the original， and the detection speed is 3.4 times of Faster R-CNN method， and which is 1.3 times of YOLOv3 method. It can quickly and effectively detect the intrusion of railway foreign object in dangerous areas of different railway scenario， and reduce the error warning.

In order to improve the effect of early lung cancer diagnosis， a pulmonary nodule detection algorithm based on deep learning architecture is proposed. A Mask R-CNN pulmonary nodule detection module based on V-Net & R-CNN， and a false-positive attenuation network for pulmonary nodules based on multi-scale and context are designed. The algorithm first uses V-Net for lung nodule localization， and then uses 3D R-CNN for false positive attenuation to determine whether the candidate region is a real lung nodule. Among them， the Inception network is introduced to design a V-Net multi-scale module to realize the localization of pulmonary nodules. A 3D R-CNN pulmonary nodule detection model was designed， and the lung nodule localization results were fused to determine the candidate regions of false-positive pulmonary nodules. The false positive attenuation network classification model designed by the design realizes the false positive judgment of lung nodules and improves the detection effectiveness. The experimental results showed that the algorithm achieves a FROC value of 0.97， which was 5% higher than that of the existing algorithms， indicating that the algorithm has good clinical significance for early detection and diagnosis of lung cancer.

To deal with the problems of insufficient precision of 3D face reconstruction methods and small number of labeled public 3D faces， a self-supervised neural network using multi-scale feature fusion and dual attention mechanism for 3D face reconstruction is presented in this paper. The proposed network， taking a single face image as input， employs encoder-decoder modules to predict the reconstruction components. The proposed multi-scale feature extraction fusion can obtain multi-level face feature information， while dual attention mechanisms are integrated into the encoder-decoders to further improve the feature extraction ability of the network. Moreover， the self-supervised scheme with a single image input bypasses the high requirements for training datasets in traditional methods. We conducted comparative experiments and ablation experiments on the BFM， Photoface and CelebA face datasets. Experimental results show， compared to the well-known 3D face reconstruction methods such as Unsup3D， the proposed method performs 10.3% better on scale-invariant depth error （SIDE）， and 12.6% better on mean angle deviation（MAD）respectively. In addition， our method is more robust to partial occlusion or missing of input image.

To acquire fine spectral and spatial features and their interaction information in hyperspectral image classification， a hierarchical spatial-spectral fusion network is proposed. Firstly， the hierarchical features extraction module was exploited to extract the spectral and spatial features of hyperspectral image respectively. Secondly， the spatial-spectral feature interactive fusion module was designed and employed to fuse the features and produce the joint spatial-spectral features. The proposed network can not only extract and integrate the fine spatial and spectral features of different levels，but also capture the interaction between spectral and spatial features by joint learning. The experimental results show that the proposed network performed better than the state-of-the-art neural network-based classification methods. The network is shown the capability of extracting fine features and capturing the spatial-spectral joint features for classification.

Increasing the size of transducer array can improve the accuracy of focusing， but it also brings the increase of power consumption and focusing noise. To solve this problem， a local ultrasound array focusing method based on multi array synthetic aperture was proposed.The number of focused transducer was determined by proposed method according to the radiation angle of transducer， and power consumption and noise were reduced by reducing the number of transducer working at the same time. The focusing accuracy and power consumption of local array focusing method and full array focusing method are tested. The results show that， compared with the full array focusing method， the focusing accuracy of the proposed method is slightly improved， the focusing intensity is reduced by 5%， but the power consumption is reduced by 31%.

Aiming at the characteristics that the multi-target radar detectors can obtain the regional traffic performance， a signal control method at isolated intersections was r proposed based on multi-target radar data. First， according to the queue length of the detection area at the approach， a signal control strategy at isolated intersections is designed， and the signal control method is divided into two stages. Secondly， the signal control methods of the two stages are determined respectively. The first stage mainly solves the problem of conventional vehicle release， and establishes the signal phase transfer rule. The second stage mainly solves the problem of excessively long queues of vehicles at the approach and overflow of queues at the exit. The corresponding control logic rules are established. On this basis， the value and optimization method of related control parameters are determined. Finally， the actual typical intersection is taken as an example to simulate and verify the proposed signal control method. The results show that the proposed signal control method in this paper can effectively improve the operation efficiency of the intersection， the average vehicle delay is reduced by 9%， and the vehicle queue length under high traffic demand is reduced by 15.5%.

According to the difference of thermal expansion coefficients between water/ice and aluminum alloy， and the phase change expansion characteristics of water during the freezing process， the freezing process of water on the aluminum alloy surface was observed. And the freezing interface stress was obtained indirectly by using the strain test method. Combined with the morphological characteristics of water in the freezing process of aluminum alloy surface， the formation law of tangential adhesion force at the freezing interface during the freezing process and the generation characteristics were analyzed. It was found that during the freezing process， the interfacial strain between water and aluminum alloy first decreased gradually as the ambient temperature decreased. When the water attached to the aluminum alloy surface began to phase change， the freezing interface strain increased rapidly. After the water attached to the aluminum alloy surface was completely frozen， the freezing interface strain tended to stabilize. The analysis showed that in the low-temperature environment， the water attached to the aluminum alloy surface entered the supercooled state. And the water and aluminum alloy substrate shrank， and the interface strain decreased. When the water started phase change， the water attached to the substrate surface began to freeze into ice. And the accreted ice bit and adhered to the material surface， the phase change expansion force overcame the shrinkage stress of the aluminum alloy substrate， and the interface strain increased rapidly. Moreover， when the attached water was completely frozen and the ambient temperature stabilized， a stable adhesion interface was formed between the ice and aluminum alloy， and the ice adhesion interface stress was formed. Finally， studying the interfacial stress characteristics of icing adhesion could help explain the formation of the law of icing adhesion and its strength. Also， it would provide theoretical support for the developing or optimizing of process intervention anti-icing technology in the engineering field.

An orchard sprayer with symmetrical fans was designed to solve the problem of serious waste of liquid waste and difficulty in penetrating droplets inside the canopy of fruit trees during the operation of pest control in low anvil Close Planting Orchard. Based on the STAR-CCM+ simulation analysis of the single side flow field， the airflow rate is 4397.1 m³/h， the wind speed is 9.7 m/s， and the wind maneuver pressure is 47.3 Pa when the speed of the tractor PTO is 540 r/min. The airflow velocity reaching the outer canopy of the fruit tree is over 5.5 m/s， sufficient to reach the center of the canopy. The Box-Behnken design was adopted to optimize the combination of sprayer system parameters. The optimal parameters are obtained： the spray pressure is 0.8 MPa， the spray distance is 1.25 m， and the nozzle type if fan-shaped 02. The adhesion of chemicals onto the fruit tree canopy in the vertical direction is simulated with a vertical droplet distribution meter. Experiment results show that the variation coefficient of droplet deposition is 10%-12%， meaning the distribution of droplets is relatively uniform. Field experiments show that the total droplet deposition on the upper， middle and lower layers of the canopy in the vertical direction is 68， 145， and 195 mL， respectively， and the overall standard deviations are 1.61， 3.72， and 5.29， respectively. The number of droplets attached to the leaves is ≥70 particles/cm²， which meets the requirements of air-assisted orchard sprayer standard， offering adequate coverage of the fruit tree canopy in the vertical direction.

Aiming at the problems of stem clamping and conveying， such as broken stems and blockages in the process of fresh corn harvesting， a bionic ear picking flexible clamping and conveying device with simple structure and stable clamping and conveying was designed. First， on the basis of analyzing the holding the stalk， bending and breaking the ears from top to bottom， researching the principle of bionic ear picking， the focus is on the operating parameters that affect the flexible stem clamping and conveying device. The grading test method is adopted for the device. The bench test was designed with a single factor， and it was obtained that the working efficiency was the best when the unit forward speed was 1.2 m/s and the cutter speed was 420 r/min. Secondly， fixing the two parameters unchanged and taking the speed of clamping conveyor belt， the inclination angle of clamping mechanism and the gap between clamping conveyor belt were taken as test factors， and the stem breaking rate and clamping accuracy were taken as test indexes， using the quadratic regression orthogonal rotation combination test method to carry out the bench test. The mathematical model of factors and indicators is established through Design-Expert8.0.6 software， and the factors are optimized after the constraints are added. The test results show that when the speed of the clamping conveyor belt is 342 r/min， the inclination angle of the clamping mechanism is 19°， and the clamping conveyor belt gap is 6.0 mm， the average value of stem broken rate is 0.46%， and the average value of clamping accuracy is 94.0%， which show a good working performance. Finally， a field verification experiment was carried out. The results show that the average stalk breaking rate was 0.55% and the average clamping accuracy rate was 93.0%， which were basically consistent with the optimized results. The device designed in this paper reduces the stalk breakage， has high stability， and meets the requirements for harvesting fresh corn ears.