Based on the indentation， geometric parameters （surface area and depth） obtained by stereo camera were used to identify pressure-sinkage model coefficient combined with least square method. Three kinds of lunar soil simulant （CE5_1， CE5_2 and CE5_3） were used in these studies and each kind of simulant was prepared based on bulk density for three states （soft， normal and hard）. There were 264 data including 96 data of CE5_1， 36 data of CE5_2 and 36 data of CE5_3. The data of each kinds of simulant was randomized in two data set according to the ratio of 3∶2， one was calibration data set， the other was prediction data set. Based on the pressure-sinkage model coefficient obtained by calibration data set， the accuracy of prediction data set of CE5_1， CE5_2 and CE5_3 were 0.985， 0.965 and 0.971， respectively. The results show that this pressure-sinkage model can be used to provide numerical reference to determine the extent of soft and hard lunar surface which can provide reference for excavation depth of lunar regolith sampler.

The in-cylinder combustion process of a two/four-stroke switchable diesel engine at high load conditions under two-stroke working mode was analyzed based on the three-dimensional CFD simulation. It was found that the movement of airflow causes the mixture to burn at the edge of the piston bowl， resulting in local high temperature and local excessive thermal load. Two optimized designs of combustion chambers were obtained to adjust the match of oil and gas with chamber to improve combustion. It was found that increasing the diameter of combustion chamber expands the diffusion space of the mixture and reduces the mixture diffused above the piston bowl edge， which can avoid excessive thermal load at the piston bowl edge. Increasing the diameter of the piston pit helps produce the vortex in the pit to promote the mixture formation and shorten the combustion duration. The thermal loads of the piston bowl edge of these two designs are significantly improved， and the combustion durations of the chamber Ⅰ and chamber Ⅱ increase the power by 4.11% and 1.96% respectively. The comparison test of the original machine and chamber Ⅰ was carried out under the ablation condition of the original machine at 2800 r/min@46 kW. Compared with the original machine， the fuel consumption of the chamber Ⅰ is reduced by 6.93%， exhaust temperature is decreased by 21.1 K and the piston surface is smooth without any ablation.

Aiming at the narrow hilly farming environment， a new type of bow waist intelligent mobile chassis was designed. The crawler driving device of the intelligent chassis can passively adapt to the terrain change， and can actively adjust the contact angle between the track and the ground to improve the adhesion performance of the track. The mechanism design and transmission principle of the mobile chassis are described in detail. On this basis， the influence factors of soil resistance and adhesion were explored when the track was driving on soft road and the relationship between the attitude change and obstacle crossing height is analyzed. The maximum vertical height of obstacle crossing that the chassis can reach in the bowed posture is 241.53 mm. Through the analysis of longitudinal climbing stability and geometric analysis of the turning process， the maximum climbing degree is 41.23° and the minimum turning radius is 1.297 m. Finally， the reliability of the theoretical analysis is verified through obstacle crossing and maneuverability tests. The results show that the designed chassis improves obstacle crossing performance after changing its attitude， and has good longitudinal slope stability and flexible maneuverability. It can meet the operation requirements in complex farming environment， and has broad application prospects in agriculture， forestry and other fields.

Aiming at the difficulty of grade quantitative classification caused by the fuzziness of metal magnetic memory （MMM） characteristic parameters among different weld grades， a quantitative classification model based on particle swarm optimization fuzzy c-means clustering （FCM） is proposed. The fatigue tensile test was carried out on the Q235 steel weld specimen prefabricated by an incomplete penetration defect. The MMM signal was detected by the TSC-5M-32 MMM instrument. The three-dimensional composite characteristic parameter vector was extracted from the experimental data. At the same time， the MMM test results were compared with the X-ray test results to provide a reference. Considering that the initial clustering center of the FCM algorithm is determined randomly， it is easy to fall into the local optimum， and the artificial setting of weight m leads to low clustering accuracy. Particle swarm optimization （PSO） algorithm with global search and high efficiency is introduced to optimize the initial clustering center and weight m of the FCM algorithm. The modified reciprocal formula of the FCM objective function is used as the fitness function of the PSO algorithm， and the sample individuals and weight m are encoded as particles. The speed and position of the particle are updated to obtain the global optimal cluster center， and m is converged to the optimal solution. The quantitative MMM classification model based on the FCM clustering center and m optimized by PSO is established for different weld defect grades. The results show that the classification accuracy of the model is 97.93%， which provides a new idea for the quantitative identification of weld defect levels and evaluation of equipment safety.

The effects of fuel temperature， working load and wearing time on the lubrication performance of RP-3 jet fuel were studied on a high-frequency reciprocating rig. Further， different lubricity additives were used to improve the lubrication performance of RP-3 jet fuel. It is revealed that RP-3 jet fuel produces larger wear scar diameter than diesel fuel， and increasing working load and wearing time elevate the wear scar diameter. Increasing fuel temperature slightly reduces wear scar diameter， possibly because of the increased generation of anti-wearing products. Lubricity additives can dramatically enhance RP-3 jet fuel lubrication performance， but the improvement effects are not monotonic with the addition fraction. As the addition fraction exceeds a certain value， the lubrication performance of RP-3 jet fuel does not obviously change with further addition of lubricity additives.

To find hard asphalts with perfect high temperature stability and low temperature crack resistance as the cementation material of high-modulus asphalt concrete， SBS modified asphalt was used to modify six kinds of natural hard asphalt. The relationship between the dosage of SBS and the dynamic modulus of the mixture was explored. The low-temperature performance of this hard asphalt before and after the modification was investigated from the three levels including asphalt， asphalt mortar and asphalt mixture. The results show that the low temperature performances of the modified asphalt， asphalt mortar， and mixture also have different degrees improvements. It is a good choice for producing high-modulus asphalt concrete. The study also found that among the many low-temperature performance evaluation indicators， low-temperature critical cracking temperature （T_CR） of asphalt mortar is the most relevant to the test results of low-temperature bending of the mixture. When selecting asphalt binder for high-modulus asphalt concrete， special attention should be paid to this indicator.

To find the way of crossing intersections efficiently with the least delay per capita at two-phase intersection， three delay models of non-motor vehicle such as traditional crossing， second crossing and motor vehicle parking line moving backward are established by arrival-departure curve and Webster motor vehicle delay model. The relationship between per capita delay of three kinds of non-motor vehicle crossing the street and the traffic flow is described by taking the per capita delay the discriminant criteria. By contrasting average delay of three kinds of motor vehicles crossing street and taking the minimum delay as the optimum crossing way， the critical value of the traffic flow is obtained， which can provide the reference for choosing valuable way of non-motor vehicles crossing the intersection.

To process the important problem of path travel time prediction， this paper proposes a deep learning network model on spatiotemporal feature. It is combined with long-short-term memory network （LSTM） and convolutional neural network. At the same time， it considers the spatial dependence of road sections， timing dependence， and time drift in coarse granularity to predict the path travel time of the next time slot. In the experiments of this paper， the dataset of Harbin taxi trajectory is taken as the test dataset. The comparison results show that spatiotemporal characteristics based deep learning network model outperforms that based on the machine learning model with multiple evaluation indicators. On the indicators of MAE and R²， the performance of the algorithm in this paper is better than other algorithms by 18.6% and 22.46% respectively. At last， the prediction accuracy of the model proposed in this paper is over 90%， and the efficiency is at the leading level among similar algorithms.

To improve the situation of high demand and low occupancy rate of taxis， the study focuses on the problem of online ride-sharing. Considering the turnover of passengers， vehicle transportation mileage， travel time， and travel cost， a mathematical model of single-vehicle carpooling matching was established. Based on the principle of fairness and considering the bilateral interests of drivers and passengers， a rate calculation method was proposed. Passenger clustering is conducted with the path fitting degree as its basis. Based on the Dijkstra algorithm， the shortest path algorithm by section with obligatory nodes was proposed. Simulation results show that， compared with the traditional taxi operation mode， online taxi-sharing has a significant effect on improving transportation efficiency and reducing transportation cost.

In order to quantify the influences of the built environment at different spatial scales on car ownership， a gradient boosting decision tree model is applied to investigate the relative importance of the built environment at both city and community levels in this study. The survey data from China Labor-force Dynamics Survey is used to conduct the empirical analysis. The results show that the individual socio-economic status is the most influential among three categories of factors with collective importance of 53.24%. With the collective importance of 30.45%， the built environment characteristics at the community level have greater influences than the built environment characteristics at the city level. For the specific characteristics， household income is the most influential factor with the relative importance of 31.66%. All built environment characteristics have influences that are greater than 1.5%. Therefore， it is important to optimize the built environment at different spatial scales to deter the increase of car ownership.

To analyze the users' preference for ridesplitting and the characteristics of changes in the size of ridesplitting under different pricing environments， this paper adopts the Revealed Preference and Stated Preference （RP+SP） questionnaire method to investigate the behavior preferences of Shanghai residents in different environments， such as changing discount rates of ridesplitting service， the growth rate of ride-sourcing fees and the cost of parking. The nested logit model is used to calibrate the influence of socio-economic attributes and pricing factors on user choice behavior. Research shows that females with a monthly income of CNY 5000 to 9999 and the age between 26 and 40 are more inclined to choose ridesplitting. Through numerical analysis， this paper simulates the trend of ridesplitting market size under the changes in ridesplitting discount， ridesourcing fees and parking charge. The analysis shows that the size of the ridesplitting market increase by 1.03%， 0.48%， and 0.16% respectively under the unit change in ridesplitting discount， increased ridesourcing service fees and changes in parking fees. The results show that respondents are more sensitive to ridesplitting discounts.

In order to overcome the risk of insufficient moisture stability of the recycled hot-mix asphalt （RHMA）, this work aims to optimize the material design of RHMA based on the performance-cost model by considering the moisture stability as the control index. First， the quadratic regression model involves independent variables （reclaimed asphalt pavement （RAP） content， asphalt content， and aggregate gradation） and dependent variable （moisture stability） was established. Second， the cost model was proposed covering the whole construction process， thereby the optimal material design model of RHMA was drawn. Finally， the optimal design parameters were calculated with the performance-cost model by jointly using the genetic algorithm and linear comprehensive evaluation method. The results show that the response surface can effectively address the material design of RHMA， and the independent variables were found significant to the moisture stability of HRMA. The proposed model was proved valid in project of the Sanming section of Fuzhou-Yinchuan expressway， the optimized cost was 26.7% lower than the actual cost at the same moisture stability control objective， and the optimized material parameters of HRMA for RAP content， asphalt content， and gradation parameter were 51%， 4.61%， and 0.445， respectively.

Based on the U-shaped hoop pin and longitudinal rib connection technology， a new type of assembled utility tunnel was proposed. Through the static load test of the full-scale model， the mechanical properties of the new assembled double-cabin utility tunnel， such as load-bearing capacity， deformation capacity， and component cracking， were studied systematically. The test results show that the connection performance of the U-shaped hoop pin and longitudinal rib connection is reliable. When the test is loaded to the design load of 435 kN， cracks appear at the end of the assembled utility tunnel. During the loading process， the long-span roof slab of the assembled utility tunnel experiences three stages： cracking， stiffness degradation and ultimate failure. Under the characteristic value of load， the crack width and deflection of the assembled utility tunnel do not exceed the specification limits， which meet the requirements of the normal service limit state. The ultimate bearing capacity of the utility tunnel is 3.71 times of the design value of load. The ultimate bearing capacity meets the design requirements， and there is a large safety reserve. The experimental research results provide a technical basis for the practical application of the utility tunnel of this new assembly scheme.

Four detachable prefabricated composite beams are designed and their mechanical properties under the action of negative bending moment are studied by taking shear connection degree and debonding treatment as test parameters. The test results show that：①the shear coupling degree can improve the anti-cracking performance of the detachable composite beam under the action of negative bending moment， but it has little influence on its ultimate bearing capacity and stiffness. ②Partial debonding treatment of anti-shear joints can improve the mechanical properties of detachable composite beams under the action of negative bending moment， delay the development of cracks， and have little influence on the stiffness and ultimate bearing capacity of specimens. However， this treatment has a more obvious effect on the improvement of specimens with complete shear connections. ③Under the action of negative bending moment， the rotation capacity of the detachable composite beam still has a large room for improvement. Both the debonding treatment and the increase of shear connection degree can improve the rotation capacity of the composite beam， which can be further optimized.

To study the wind pressure distribution characteristics of double-sided spherical shell roofs in flat and sloping terrain under the action of downbursts， a rigid scale model was made based on the similarity criterion. Through wind tunnel tests， the influence of the change of the flow field position of the model in the two terrains on the distribution of the average wind pressure coefficient of the roof and the position and value of the maximum and minimum values are studied. The results show that as the radial distance increases， the average wind pressure coefficient on the windward side of the upper roof changes from positive to negative， and the maximum negative value appears at the edge of the windward surface. The value of the face-on average wind pressure coefficient of the lower roof gradually decreases， but always remains positive. As the radial distance increases， the average wind pressure coefficient of the upper roof is always negative and the negative value gradually increases. The average wind pressure coefficient on the windward side of the lower roof changes from positive to negative， and the maximum negative value appears at the edge of the windward side. The location of the model in the wind field and the different topographic conditions have great influences on wind pressure distribution characteristics which include the positive and negative wind pressure transitions of the upper and lower roofs and the location of the extreme wind pressure and its numerical values.

In order to improve the accuracy and stability of local time series link prediction for cross social network， a Hadoop based local time series link prediction algorithm is proposed. This algorithm selects six cross social network node similarity indicators， and designs a parallel computing model using the core component MapReduce of Hadoop， which can segment and process the massive parallel data in cross social network， and reduce the computational complexity. Based on this， the local time series link prediction algorithm based on MapReduce parallel operation model is used to obtain the point-to-point prediction score in the network by using the selected node similarity index， so as to realize the prediction of cross social network local temporal link. The experimental results show that the prediction accuracy of the proposed algorithm is high， it can maintain a stable prediction state， and has good comprehensive prediction performance.

Aiming at the issue of peak classification in melting curve images， a convolutional neural network （CNN） classification model based on multi-scale fusion is proposed. First， the multi-scale context information obtained through dilated convolution is integrated with the feature information extracted from the residual module， which makes up for the shortcoming of the deep network that loses the global information. Then， different from traditional convolution with constant convolution kernel， a dynamic filter that changes with the input is used to make the network learning more accurate. In addition， in order to train the model， a dataset of melting curves is created， which includes a balanced dataset and an unbalanced dataset. This paper uses six classification-based evaluation indicators to compare with six classification methods based on deep learning. Experimental results show that the proposed method is significantly better than other methods in objective evaluation indicators.

Due to the complex characteristics of medical images， for example， the lesion region possesses an irregular shape and its scale can greatly vary， the intensity of surrounding tissues is inhomogeneous and the boundary is blurred， which reduce the accuracy of medical image segmentation， a medical image segmentation algorithm based on multi-scale context-aware and semantic adaptor is proposed. In order to improve the representation ability of feature learning， the multi-scale context-aware module is utilized to learn rich context information from multiple receptive fields， and dynamically assign the weight of semantic features at different scales according to the size of the target region. The multi-level semantic adaptor module is adopted to aggregate multi-level abstract semantic features and spatial details to refine the boundary of the target region and reduce the feature gaps between encoders and decoders. The algorithm proposed in this paper is compared with other algorithms quantitatively and qualitatively on three public medical image datasets of different modalities. The experimental results show that the proposed algorithm is superior to other algorithms in various complex scenarios of medical image segmentation tasks.

In order to address the challenges caused by limited medical resources in large-scale Diabetic Retinopathy （DR） screening process， a dual-branch hybrid attention decision net （BiRAD-Net） for DR classification is proposed. The proposed network consists of feature extraction and classification two stages. In the feature extraction stage， a hybrid attention is introduced to suppress the noise， and feature grade decision network is designed to improve feature quality. In the feature classification stage， dual-branch classifiers and corresponding dual-branch loss are designed to alleviate the impact of insufficient data with five-category labels and enhance the classification accuracy. Furthermore， the transfer learning technology is applied in the training process， together with the above steps， to improve the accuracy of model and reduce the amount of training data. Experimental results on KAGGLE dataset indicate that BiRAD-Net shows excellent diagnostic ability for all the stages of DR and preforms better than other existing comparison methods.

To overcome the deficiency of traditional feature pyramid network （FPN） used in the field of panoptic segmentation， a Two-way FPN for panoptic segmentation （T-FPN） is proposed. The network solves the contradiction between the unified feature output caused by the one-way transmission of FPN and dual-threaded task feature requirements of panoptic segmentation. Based on the analysis of the difference between the foreground and background of the image， the two-way transmission paths are constructed according to the feature requirements of the foreground and background segmentation tasks， the top-down path uses upsampling to strengthen foreground feature， and the bottom-up path enhances background feature with dilated convolution. The two-way network is able to extract foreground feature and background feature simultaneously， which is helpful to create a dynamic balance of the foreground and background segmentation accuracy， thereby improving the quality of panoptic segmentation. The experimental results on the MS COCO and Cityscapes datasets show that our T-FPN is superior to the existing similar methods in terms of segmentation accuracy. Compared with UPSNet that uses traditional feature pyramid network， the PQ value of T-FPN is increased by 0.47%.

In order to improve the performance of agents in text games， a deep reinforcement learning model called SADSR based on siamese network and deep successor representation was proposed. Firstly， the model uses natural language processing technology to process text information to obtain the embedding vector of words， which can effectively transform the text information into digital vector. Then the siamese network is used to extract the features of state and action information， and the extracted state feature vector is used to predict the immediate reward， and the joint vector of state and action is used to predict the successor representation. Finally， the action value is calculated by the interaction function between the successor representation and the weight vector of the specific layer. The experimental results show that the model can effectively fit the value function. Compared with the current mainstream models， SADSR can improve the performance of agents in text games by 10%~60%.

Using the deep learning methods can solve the model over-fitting caused by less observation data, and improve the prediction accuracy. This paper proposes spatio-temporal model of soil moisture prediction integrated with transfer learning. Firstly, the EAR5-land dataset is used as the source model. Then three-dimensional layer convolution is used to extract the spatial characteristics of the lag time of the soil moisture, and the long short-time memory network is integrated to extract the temporal characteristics. Third, the network model is pre-trained. Finally, the fine-tune method is applied to adjust the network parameters in the SMAP dataset for soil moisture prediction. The experimental results show that the proposed model has the better prediction results than the convolutional neural network, long short-term memory network and PredRNN. Meanwhile the method of transfer learning can improve the prediction accuracy.

At present, China's drug testing laboratories conduct their own tests independently, and there is no information exchange between the laboratories, forming a large number of drug testing information islands, which hindrance the problem of drug testing data sharing and research and early warning of parallel cases. This paper proposes to use alliance chain to solve the data sharing of drug testing laboratories, and analyzes the problem of consensus algorithm of drug testing alliance Block chain. Aiming at the consensus algorithm problem, this paper proposes to optimize the consensus algorithm of drug testing blockchain based on Cultural Genetic Algorithm. Firstly, Genetic Algorithm (GA) is modified to form efficient HGA algorithm based on Hash function. Then, an efficient Cultural Genetic algorithm CHGA is formed by introducing Cultural Algorithm to transform HGA. Then combining Pareto multi-objective optimization technology to improve the efficient multi-objective optimization Cultural Genetic Algorithm PCHGA algorithm, and using PCHGA algorithm to solve the Byzantine fault-tolerant consensus algorithm, the formation of multi-objective optimization cultural genetic algorithm practical Byzantine algorithm PCHGA-PBFT. The problem of consensus node candidate set and primary node election of PBFT is solved with the algorithm above, and the available algorithm of PBFT consensus optimization is obtained.The simulation results show that the proposed algorithm achieves the desired results in the selection of consensus nodes and primary nodes.

In order to solve the problems of poor grain crushing effect and low crushing rate of domestic silage corn harvester, which affect the nutrient transformation of silage, a corn grain bonding contact model was established based on discrete element method, and the corresponding bonding model parameters were determined. Combined with the principle of differential grain crushing, the effects of crushing roller gap, upper and lower crushing roller speeds on grain crushing rate of silage corn were explored. The results show that when the upper crushing roller speed was 5197 r/min, the lower crushing roller speed (differential ratio) was 3949 r/min and the crushing roller gap was 3 mm, the average crushing rate of bonding bond of corn grain discrete element model reached 95.35%. The results show that the grain crushing rate of silage corn was 92.1%, about 75.3% of silage corn grains were less than 1/4 of the normal size; 19.5% silage corn grains were larger than 1/4 of normal size. The relative error between simulation test and bench test is 3.25%. It meets the fermentation and nutrient transformation standards of silage, and provides a new research method for the development of grain crushing technology and equipment.

To solve the problems of unreasonable design and unsystematic analysis of durability test in agricultural machinery testing ground， a design method for tractor fatigue accelerated structure test was proposed. Taking the construction of optimization matrix for accelerated structural test design as the core， the time-domain extrapolation method based on Peak Over Threshold （POT） model， the augmented Lagrangian multiplier method for solving the optimal matrix and the Monte Carlo method for sensitivity analysis were combined. Then， the method was verified by taking standard tractor test site as an example. The results show that the optimal number of repetitions/turns for the four simulated working conditions are 0， 1885， 2392 and 241 respectively. The total duration of the combined test is only 134 h， the acceleration coefficient is 3.2， and the average relative error of damage is only 23.14%， all indexes are better than the traditional fatigue durability test. Compared with the results of Monte Carlo method， the errors of the optimal repetition times of the simulated working conditions are less than 5%， which verifies the accuracy， rationality and effectiveness of the design method of tractor durability accelerated structure test.

In order to improve the crashworthiness of thin-walled absorber, a new type of bionic multi-cell tube was designed based on dactyl club microstructure of O. scyllarus. The crashworthiness of bionic multi-cell tubes with different herringbone ratios η (the ratio of herringbone height A and width λ) were comprehensively investigated under different loading angles (θ=0o, 10o, 20o and 30o, respectively). The bionic multi-cell tube presents progressive folding deformation mode under axial (θ=0o) and small oblique loading angle (θ=10o). Compared with axial loading condition, the bionic multi-cell tubes have larger speci?c energy absorption E_s and crush force ef?ciency C_f, but smaller peak crush force F_p when θ is 10o. A complex proportional assessment method was applied to solve this multi-criteria decision problem. The result shows that the bionic multi-cell tubes have superior crashworthiness when their η ranges from 0.6 to 1.0, and from 1.5 to 1.7, and η=1.5 was selected the best sectional con?guration herein. Following such optimal selection, a metamodel-based multiobjective optimization method based on polynomial regression metamodel and multiobjective particle optimization algorithm were adopted for the dimensions design of the optimal selection. The optimal parameters of thickness t ranges from 0.75 mm to 1.2 mm, element width λ ranges from 5.5 mm to 9.5 mm, the initial peak crush force F_p and maximum speci?c energy absorption E_s is 59.8 kN and 13.28 kJ/kg, respectively. The bionic design and optimization method in this work hope to provide a reference for the lightweight design of thin-walled energy absorber.