For solving the problem of car-following model parameter calibration and determining the appropriate evaluation method of model simulative effect,discussed the method of calibrating parameters with application of genetic algorithm,and estimated the parameters of GM model with measured data.Base on the results of calibration and the meaning of parameters,designed the specific method to test effect of car-flowering model simulation considering the fit level of the whole car-following process between the car-following model simulation and actual driving situation,and validated the calibrated GM model.The results of research show that,the test method presented solve the limitation of validating the car-following model before,and can measure the effect of model more comprehensive and objective.Use this method to validate the GM model,the results show that,the integrated error between simulation output and measured data is less than 15% for most process,not the big error from the only contrast of acceleration.

As one of the important contents of bridge health detection， crack detection reflects the stress and damage state of bridge structure. The traditional bridge crack detection is mainly based on human eye recognition， of which efficiency and accuracy are both low. Moreover， the human eye recognition has the following problems such as effected greatly by illumination， incapability to detect in some high-altitude positions like bridge towers and high piers and strong subjectivity. In recent years， many scholars at home and abroad have developed many bridges crack detection equipment based on digital image technology to solve the above problems， such as bridge detection vehicles equipped with high-definition cameras， drones， and climbing robots. Meanwhile， the efficient and high-precision crack detection algorithm is the basis of crack detection. How to balance the detection speed and accuracy has always been one of the hot issues studied by many scholars. In this paper， the bridge crack detection equipment based on digital image technology， the platform and calibration method of camera， preprocessing algorithm， traditional detection algorithm， deep learning algorithm， crack feature calculation， image stitching algorithm and three-dimensional output and monitoring of cracks are reviewed. In addition， summaries to deficiencies in the study and prospects the bridge crack detection method， crack three-dimensional expression， crack monitoring and management， bridge stiffness loss evaluation and early warning for the future development trend.

A Bayesian network for traffic accident causation analysis was developed by structure and parameter learning,using correlation analysis,K2 algorithm and Bayesian method.Based on the Bayesian network,the interaction mechanism between the causing factors and the casualties of traffic accident was infered,and the effect of traffic control improvement on accident casualties reduction was analyzed.The results show that the Bayesian network can express the complicated relationship between the traffic accident and the causes,as well valuable information on how to take effective measures to reduce casualties of traffic accident.Moreover,the model has a high accuracy.The study can contribute to the development of traffic accident causality theory and the improvement of traffic safety situations.

In this work， the mechanical contact model between normal triangular atomic force microscopy（AFM） probe and coating film was established， which aims to measure the elastic modulus of polyurethane coating film. Whilst the force-indentation depth relationship between the probe and the coating film was obtained by analyzing the AFM's mechanical test curve， and the elastic modulus of the coating film was determined by combining the newly established model. In addition， the microstructure of the coating film was tested using positron-annihilation technology， and compared with its elastic modulus. Finally， the grey correlation theory is used to analyze the correlation between the microstructure parameters and the elastic modulus of the coating film. The results show that the elastic modulus of the coating film was measured within the region of 6.118 MPa to 6.917 MPa. This is consistent with the research results of the elastic modulus of polyurethane coating in existing literature， which guarantees the validity and accuracy of the present method. The elastic modulus of the coating film is negatively correlated with its free volume aperture and free volume fraction since that the restriction ability of the substrate to its molecular chain segment movement is significantly lowered with the increase of free volume size and content. Among them， the free volume average pore diameter of the coating film has higher effects on its elastic modulus.

In order to promote the application of road safety intelligent perception technology， the research progress about intelligent monitoring of pavement icing condition based on optical fiber sensing at home and abroad was overviewed. Firstly， the principle of fiber-optic pavement icing detection was revealed. Subsequently，based on the analysis of different optical fiber performance indexes， the distribution mode of common probes and various weak signal detection methods， the fiber-optic pavement icing detection system was constructed. Then， common icing detection data pre-processing and its thickness analysis methods were explored， the main environmental influencing factors and measures to enhance detection effectiveness were outlined， and burying technology of fiber-optic road icing detection sensor was analyzed. Finally， current research status of icing pavement monitoring and early warning were summarized， the existing problems were discussed， and the development direction of intelligent sensing of pavement icing condition was attempted to outlook.

Since the concept of resilience was introduced into the field of transportation， resilient transportation has received extensive attention from researchers in the transportation field. Road transportation infrastructures with good resilience can effectively deal with various natural and man-made disasters， and further meet the needs of efficient and safe transportation in the future. In order to clarify the current research status of road transportation infrastructure resilience around the world， the research results of road transportation infrastructure resilience were summarized from the definition， measurement methods， and resilience improvement technologies of road transportation infrastructure， and the future road transportation infrastructure resilience research was discussed especially the development direction and research focuses of resilience improvement technology. The analysis shows that the research on the resilience of road transportation infrastructure mainly focuses on the overall traffic network organization and planning level of the road network. The research on the resilience of the infrastructure structure is relatively scattered， and there is a lack of unified and comprehensive definitions and metrics. In addition， in the research on the catastrophic failure mechanism of structural resilience， there is a lack of a comprehensive research understanding of all elements and the coupling between structural systems， and it is difficult to reveal the chain process and catastrophe characteristics of catastrophic evolution. Therefore， the research on the resilience of future road transportation infrastructure should further reveal the theories and methods of catastrophe analysis of different types of facility structures， and establish a comprehensive and unified definition and measurement standard for the structural resilience of road transportation infrastructures. At the same time， from the perspectives of disaster monitoring， structural safety and resilience improvement， flexible operation and post-disaster recovery， etc.， more effective technologies for improving the resilience of road transportation infrastructure should be formed and furtherly promoted in the future.

Topology identification of distribution network is an important work to ensure the safe and stable operation of distribution network. It could provide structure data for system power flow calculation， load capacity distribution， fault diagnosis， power network state estimation， which is the foundation of distribution network system analysis.The existing research of distribution network topology identification into two categories could be divided in this paper ： the first type of method is based on historical topology information， including matrix method， innovation graph method and optimal matching method. The second type of method is based on real-time measurement information， including correlation judgment method， signal injection method， linear programming method and machine learning method. Finally，the application range， main used data and characteristics of the existing methods was analyzed，the future research direction of distribution network topology identification was proposed.

In order to improve the overall travel efficiency of urban rail transit， an optimization algorithm for urban rail transit operation scheduling based on linear programming was proposed. By extracting the characteristics of rail transit road conditions and passenger flow， a scheduling optimization model that fits the actual situation was established. Linear programming was used to transform the scheduling optimization model into an integrated scheduling optimization model for urban rail transit with significantly reduced infinite discretization probability. The experimental results show that the average delay time after optimization of the proposed method is reduced by 17 min， the travel time of passengers is reduced by 25 min， and the coincidence rate between rail stations and passenger flow demand points is high， indicating that the scheduling effect of the method is good.

Based on the analysis of the stress mechanism and failure mode of stud shear connectors， the differential equation of the deflection line of stud shear connectors is established based on the theory of single pile under lateral load in elastoplastic foundation. According to mechanical characteristics in different stages of the stud shear connectors， the boundary conditions are introduced， the stud shear connectors of flexural differential is analyzed， and the calculation formulas of the shear bearing capacity， shear stiffness and elastic stage at the end of the slip value of the stud shear connectorscalculation formula are put forward. The load-slip tri-fold constitutive model of stud shear connectors is established, which has the characteristics of simple mathematical form and clear physical meaning. The results show that the calculated values of shear capacity， shear stiffness， elastic end slip and ultimate slip of stud shear connectors are 85.44 kN， 64.92 kN/mm， 0.64 mm and 6.13 mm respectively and the calculated values are in good agreement with the experimental values. The three-fold line load-slip relationship curve of stud shear connectors is close to the full-curve load-slip curve fitted by Buttry and Ollgaard， and it is in good agreement with the test load-slip curve of stud shear connectors.

The remote sensing image change detection model which is based on multi-scale fusion is proposed to accurately identify the change region of the bi-temporal remote sensing images. First， a multi-scale input pyramid is constructed in the feature extraction stage to receive multi-layer receptive fields and enhance the perception of all information. Then， in order to make a tradeoff between locating the changing area and mining details， the multi-scale calculation is carried out for deep difference features. Finally， the semantic change information can be identified and retained to a great extent by integrating the different feature results of the network. The experimental results show that the proposed model has good performance in both subjective evaluation and objective indexes.

To clarify the research progress of conductive asphalt concrete technology， the classification of conductive materials and their applications in conductive asphalt concrete were reviewed systematically. The conductive and road performance of various conductive asphalt concretes were summarized. The mechanism of conductive asphalt concrete （conductive mechanism， piezoresistive mechanism and electrothermal mechanism） was discussed. The functional performances of conductive asphalt concrete such as self-monitoring， self-sensing， self-healing by induction heating， and deicing were explored. The functional application of conductive asphalt concrete in actual engineering was introduced， and the future development directions were prospected.

A feature selection algorithm based on random forest (RFFS) is proposed. This algorithm adopts random forest algorithm as the basic tool, the classification accuracy as the criterion function. The sequential backward selection and generalized sequential backward selection methods are employed for feature selection. The experimental results on UCI datasets show that the RFFS algorithm has better performance in classification accuracy and feature selection subset than the other methods in literatures.

In order to solve the problem that mechanical weeding between rice plants depends on the alignment of the machine and the seedling belt， an inter-plant weeding machine without avoiding seedlings was designed. The rear weeding component adopts the rake method to remove weeds. It controls the start and stop of the vibration operation in real time according to the front row recognition result. A tactile perception method was proposed to establish a rice plant posture recognition system. Firstly， the data of the roots of the rice plants on the front row of flexible brushes was obtained through the changes in the voltage data caused by the changes in the air pressure by the air pressure sensor. Then， the kurtosis feature and the mean feature in the data were extracted. Finally， the feature vector was substituted into the support vector machine to construct a rice plant posture recognition model. In this paper， a hydraulic motor and rear weeding components are used to form a vibration system， ensuring weeding components can move laterally. When the sensing system detects that the rice plant is fell down， the vibration system will stop working. The field test shows that the recognition accuracy of rice plant lodging is 83.11%. When the sensing system is not working， the weeding rate of the vibration system is 58.11%， and the seedling damage rate is 21.39% .The weeding rate with neither the sensing system nor the vibration system working is 27.89%， and the seedling damage rate is 4%. The weeding rate of the operation with sensing system and the vibration system at the same time is 56.17%， and the seedling damage rate is 5.83%. Based on the test results， the simultaneous working mode of the sensing system and the vibration system is more suitable for the removal of inter-plant weeds in the paddy field.

Aiming at the problems of over-redundancy of low-quality data， difficult mining of feature data and time-consuming manual recording in the traditional data acquisition and analysis process， a driving behavior data filtering method based on dynamic time regularization algorithm under man-machine co-driving was proposed. Firstly， a dynamic time warping algorithm model was built in Python environment to realize real-time deviation calculation of two sequences by means of rolling time window. Then， considering the statistical characteristics of different distance calculation methods， the deviation threshold of triggering records was designed， and the model was optimized with the global constraint of structured path. Finally， simulation analysis and real vehicle test were conducted to compare the data filtering methods under different constraints. It was found that the proposed filtering method based on Sakoe-Chiba constraints can automatically filter out an average of 53.15% of invalid data in the data preparation stage， saving 1.87 TB of data storage space per hour， the effectiveness and feasibility of the proposed method is verified.

This paper investigate the design of the controller of Automatic Train Operation (ATO) system under the consideration of multiple optimal objectives and constraints. Based on a nonlinear multi-point model, an ATO Nonlinear Model Predictive Control (NMPC) algorithm is proposed to meet the punctuality of train operation, energy saving and passenger comfort. Moreover, the theoretical analysis of algorithm feasibility and the proof of stability for closed-loop system are presented. The validity of the algorithm is verified by numerical simulation. The simulation results show that the proposed algorithm has better control effect and lower error than the Linear Model Predictive Control (LMPC) algorithm when the train meets the operational constraints.

In order to improve the power performance and economy of fuel cell vehicles， a fuel cell vehicle based on AVL-CRUISE was modeled， and the energy management strategy of fuzzy control based on Simulink was established. Then， the fixed gear was optimized into a two-speed AMT gearbox based on Isight/Cruise co-simulation. Simulation results show that the established fuzzy control energy management strategy is effective. Compared with the rule-based energy management strategy， the economy is improved by 16.4% and 8.5% respectively under NEDC and WLTP conditions. Compared with the fuel cell vehicle without optimized transmission ratio based on fuzzy control， the fuel cell vehicle with optimized transmission ratio based on fuzzy control has improved economy by 1.1% and 2.8% respectively under NEDC and WLTP conditions.

In order to study the thermal dissipation performance of a liquid-cooled plate with parallel channels， the three-dimensional steady-state analysis was performed by using CFD method. The effects of coolant flow rate， channel width， depth and layout of enhanced heat transfer structure on the performances of a liquid-cooled plate were contrastively investigated， including the thermal dissipation， the uniform temperature and energy consumption. The results indicate that the thermal dissipation performance is improved by increasing the coolant flow rate， but excessive flow rate leads to increased energy consumption and limited improvement effect. Designs of decreasing channel width from the center to two sides， decreasing channel depth and adding enhanced heat transfer structure are all beneficial to the thermal dissipation and temperature uniformity of the liquid cooling system. In addition， the design of wholly added enhanced heat transfer structure （S1） reduces the average temperature and maximum temperature difference by 8.9 °C and 9.06 °C respectively， compared with the design of equivalent channels width （A5）. The conclusions provide a theoretical direction for structural design of battery thermal management system.

The optical motion capture systems are extremely expensive for abnormal gait analysis, and the Kinect is a potential alternative equipment for its low?cost and convenience. The development of abnormal gait analysis is reviewed from four aspects: pathological characteristics of abnormal gait, abnormal gait data set, reliability of Kinect, and abnormal gait recognition method. Firstly, the abnormal gait and its pathological characteristics were summarized, and the common gait features and gait events in gait analysis were introduced. Then, the abnormal gait data sets collected by Kinect, wearable and pressure sensors are introduced. The feasibility of skeleton data collected by Kinect in gait analysis is discussed according to the existing experimental studies to verify the reliability of Kinect. Finally, the development of gait analysis is reviewed in detail from two aspects of abnormal gait feature extraction and abnormal gait classifier, and the shortcomings and development direction of current research are pointed out in practical application.

In order to improve the dynamic response performance of the air system of the proton exchange membrane fuel cell， the one-dimensional simulation model of the fuel cell system was established by AMESim software. The process of the air dynamic response of the fuel cell was simulated， and the air dynamic control strategy was optimized. The optimized control strategy was tested and verified on the fuel cell system bench. The test results showed that the new control strategy successfully achieved the decoupling of air pressure and flow， which greatly reduced the air starvation and avoided the low voltage of the stack during the dynamic loading process of the fuel cell system. The new control strategy improves the dynamic response rate of the fuel cell system.

The research status of friction stir welding（FSW）temperature field finite element simulation method and experimental measurement method are summarized systematically. The statistical analysis of papers based on finite element simulation method is carried out from three aspects ： heat source model， computational solid mechanics（CSM）， and computational fluid dynamics（CFD）. For experimental measurement method， the characteristics of different temperature measurement methods are summarized by discussing the temperature measurement principle and research routes of thermocouple and infrared thermal camera. This paper analyzes the advantages and disadvantages of the above methods， and puts forward the future research direction.

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

A pulse wave classification algorithm of low complexity based on aliasing of time-frequency domain feature was proposed to solve the problems of low recognition rate and complex implementation in current research of pulse wave signal recognition. First, the time domain features of the pulse wave signal are extracted based on the Convolutional Neural Network(CNN), including the single-period feature characterizing the features of the signal segments in the period and the multi-period features characterizing the relationship between the cycles. Then the features in the frequency domain are expressed by the Mel cepstrum coefficients based on the wavelet transform. Finally, based on the aliasing and redundancy elimination of the time-frequency domain features using the neural network full connection layer, the pulse wave signal classification is realized by the Softmax classifier. The method can achieve feature extraction through low computational cost, due to the weight sharing and dimensionality reduction of CNN. In the simulation experiment based on Python platform, the recognition accuracy of the proposed method can reach 93%, which is much higher than the accuracy of traditional recognition algorithms based on time domain features or frequency domain features.

In order to solve the problems of low detection integrity and poor detection effectiveness， a moving target detection method for video SAR Based on machine vision is proposed. Firstly， the moving target pixels are matched by Gaussian mixture model to obtain the target region of video SAR moving image. Secondly， the shadow generated by moving target is removed by HSV model and reflectivity algorithm. Finally， the processed target region is input into Yolo algorithm to complete the final video SAR moving target detection. Experimental results show that the proposed algorithm has high detection integrity， high detection rate， low false detection rate and better detection effectiveness.

To solve the problem of limited aperture of traditional uniform circular arrays， a co-prime circular array is designed， and on this basis， a two-dimensional direction of arrival estimation method based on phase mode excitation is studied. First， two uniform circular sub-arrays with a distance between array elements greater than half a wavelength are stacked to form a relatively prime circular array structure. Then， based on the phase mode transformation algorithm， the element space problem is transformed into the beam space. The algorithm combines the co-prime circular array into a Vandermonde structure similar to ULA and the Hermitian center symmetrical array flow pattern， reducing the dimensions of spectral peak searching， realizing array flow pattern through unitary transformation， reducing algorithm complexity. Theoretical analysis and simulation experiments show that under the same conditions， compared with the uniform circular array MUSIC algorithm， the proposed phase mode excitation MUSIC algorithm for the relatively prime circular array reduces the running time from 1.825 s to 0.622 s， increases the array aperture MN/（M+N-2） times， improves the DOA estimation accuracy and the real-time performance of the system， and it has a higher resolution for the estimation of similar sources.

This paper introduces the variable density based topology optimization into the design of transmission housing structure. The optimization takes the constraints on manufacturing condition into consideration. In order to increase the strength, stiffness and nature frequency of the transmission structure and control its weight, the static and dynamic topology optimizations of the transmission housing are carried out. According to the results of the topology optimization and the manufacturing constraints, a new rational transmission housing structure is designed. The stress analysis and modal analysis of the new design are carried out. The analysis results show that not only the stress level of the designed housing is lower than the material yield stress, but also the modal frequency avoids the powertrain system resonance.

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.

State of the art of the aging mechanism， characterization and evaluation of CRMA at home and abroad are summarized. The aging process of CRMA is accompanied by swelling， degradation reaction of crumb rubber and the interaction between asphalt and crumb rubber. The aging behavior of CRMA is mainly characterized from the chemical components， molecular structure， molecular weight and surface morphology， and their effects on macroscopic properties are explained. Rheological parameters such as viscosity， complex shear modulus， creep stiffness at low temperature and fatigue life are mainly used to evaluate the aging performance of CRMA， which are usually combined with microscopic characterization and chemical methods. However， during the aging process of CRMA， the migration behavior of fillers， e.g.， carbon black， white carbon black， received less attention. In addition， there is a lack of simultaneous characterization method for multi-component aging characteristics of CRMA. There are no universal aging evaluation indexes for CRMA， and most of the studies are insufficient in the aging performance verification at mixture level. The development trend and prospect of aging mechanism， characterization and evaluation of CRMA are proposed.

Beam pumping units are main oil producing equipments in land-based oilfield. Oil producing energy consumption caused by beam pumping unit accounts for one third of the total energy consumption in oilfields. However， due to the structure of the system and operating conditions， the average load rate of pumping unit motor is very low， which is generally below 30%. In order to improve the utilization efficiency of oil recovery energy， many new technologies and methods have been put forward and tried by scientists and technicians， which have got good results； meanwhile， limitations and technical risks exist. Firstly， main sources of energy consumption are analyzed， and it is pointed out through comparison that the motors have the most potential in energy-saving. Then， the researches on improving electric efficiency of motor recently reported in public are classified and summarized. Relevant technical researches can be divided into three categories： （1） Improvement of mechanical transmission structure； （2） improvement of motor and its control technology； （3） adding energy-saving devices. Based on the background of offshore oil exploitation and shale oil and gas exploitation， through summarizing and analyzing various technical schemes， it is proposed that adding energy-saving devices to traditional pumping equipment will be the most effective technical direction for beam pumping units in the future. Among them， hydraulic hybrid energy-saving technology has more obvious advantages.

In order to improve the hydrogen utilization rate of fuel cell engines， a fuel cell anode loop model was developed based on the AMESim platform,and completed model integration and calibration. Based on practical engineering experience, a new purging strategy that correlates anode loop temperature, pressure and reaction current was proposed. Using the anode loop model of the fuel cell engine, the parameter optimization boundary was calculated, and the optimization and fitting of the purge control parameters was completed. According to the simulation results， compared with the original purge strategy， the new strategy can make the hydrogen consumption rate decrease from 0.866 kg to 0.8 kg per 100 km under NEDC cyclic condition， and the theoretical economy can be improved by 7.5%. Then the new strategy was verified on a fuel cell vehicle， and the hydrogen consumption rate is 0.81 kg per 100 km， which is basically consistent with the simulation results. The driving range of the vehicle is increased from 484 km to 510 km， by 5.4%. And the effectiveness of model-based purge strategy optimization scheme is verified.

Crack disease is the most common disease of bridges， DeepLabv3+ segmentation model puts forward a new Encoder-Decoder structure among deep learning methods. It combines the high-level semantic information and shallow features of the target， and adopts the method of deep separation and convolution， which achieves superior image segmentation effect. However， in the training process of the coding module， the spatial dimension of the input data is gradually reduced， resulting in the loss of useful information， which brings some limitations to the recognition of small targets with different scales. In order to improve the segmentation performance of the network， this paper proposes an image segmentation method based on improved DeepLabv3+.By adding Yolof module and Resnet module， the receptive field is further expanded and more accurate crack feature map is obtained at the same time. In order to verify the effectiveness of the improved algorithm， a large number of actual bridge crack images are taken as the original data set， which is compared with the current representative image segmentation models such as Mask R-CNN and DeepLabv3+ on the same dataset. The results show that the algorithm in this paper improves the accuracy of crack pixels by 12% and 8% respectively compared with Mask R-CNN and DeepLabv3+. The average pixel accuracy is 91.99%， and Mean Intersection over Union is 81.43%， which is more suitable for the task of bridge crack segmentation and has practical engineering application significance.

The precision of traditional precision positioning stages are limited due to mechanical friction， so it is difficult to meet the motion requirements of large stroke and high precision. In this paper， a single drive rigid-flexible coupling positioning stage combined with macro- micro composite structure was proposed. Compared with the other macro-micro composite stage， it greatly simplified the design of mechanical structure and motion control system. Based on the floating coordinate method and finite element method， the static and dynamic model of the single drive rigid-flexible coupling positioning stage was established. The analysis results of these models were compared with the static analysis results of ABAQUS finite element software and the analytical solution of the simplified dynamic model， the maximum relative errors were 1.6% and 3.72% respectively. It is proved that this theoretical model has high prediction accuracy， which can provide parameters for the optimal design and precision motion control of the single drive rigid-flexible coupling stage.

The objective of this study is to analyze the drying shrinkage behavior of cement-stabilized macadam（CSM） and improve its anti-cracking performance. In this paper， previous studies conducted on the mechanism of shrinkage and cracking in CSM and shrinkage mitigation strategies are reviewed， and summarized. The drying shrinkage performance of CSM materials is mainly studied through shrinkage mechanism and shrinkage prevention and improvement measures. As a cement-based material， the existing research on the drying shrinkage mechanism of CSM mainly refers to the available dry shrinkage mechanism of cement concrete. Four models are always used to characterize the shrinkage at different stages according to the change of relative humidity（RH） inside the mixture： capillary tension， surface free energy， disjoining pressure and interlayer water movement. The drying shrinkage mitigation strategies of CSM have achieved a series of results in recent years. According to the different mechanisms CSM shrinkage performance improvement methods can be classified into gradation， cement content， CSM molding method， and additive modification. The results show that reducing the amount of cement， adopting the skeleton of dense gradation， employing vibration stirring or vibration compaction， and adding rubber， steel slag， fiber or other modifiers can effectively reduce the drying shrinkage of CSM and improve its shrinkage performance and road serviceability. Additionally， the shrinkage mechanism， comprehensive optimization method， drying shrinkage measurement and evaluation method and shrinkage prediction model of CSM are assessed. The development trend and prospect of the shrinkage performance of CSM are generated.

Aiming at the problems of fixed network topology， low dimensionality， uneven data distribution and low correlation， the training effect of intrusion detection model in industrial distributed environment is poor. In this paper， Fedformer， a federated deep learning algorithm based intrusion detection model for industrial Internet of Things （IOT）， is proposed. Firstly， the encoder structure of Transformer network model is introduced and improved， and the convolutional neural network and gated cyclic unit are integrated， and the intrusion detection model for industrial IOT is constructed by using the attention mechanism. Secondly， the detection model is integrated with the federated learning framework， which allows multiple industrial IOT to jointly build a comprehensive intrusion detection model. Under the premise of protecting the privacy of local data， the detection accuracy of industrial IOT network attacks is improved and the false positive rate is reduced. Experimental results show that the detection accuracy of Fedformer in the industrial network environment is 98.09%， and the false positive rate is reduced to 8.31%.

To solve the inverse kinematics of humanoid redundant manipulator， an optimized method was proposed. The analytical solution of the inverse kinematics was established by introducing the arm angle parameters， and the feasible range of the arm angle under different forms of analytical solutions was further analyzed according to the joint limit. In addition， an objective function considering joint limit avoidance and optimal energy consumption was proposed. Tasks， priority was adjusted through adaptive weight parameters， and the particle swarm optimization algorithm was introduced to obtain the optimal solution. Experimental results show that the algorithm can obtain all solutions for a given arm angle and can select the optimal solution that satisfies the joint limit avoidance and low energy consumption. The proposed method has positive application significance for the trajectory movement of the redundant manipulator.

Aiming at the lack of depth information and incomplete spatial structure information of behavior and posture in 2D images， a 3D human joint point recognition method based on weak supervised migration network is proposed. Firstly， an end-to-end 3D human pose estimation framework for real images is proposed. The depth neural network is trained with 2D and 3D mixed label images. In the 2D human pose recognition sub network， the depth regression module is added to improve the 2D human pose recognition sub network to solve the problem of depth ambiguity in 3D human pose recognition； Secondly， in the 3D human pose recognition sub network， 3D geometric constraints are introduced to standardize the human pose recognition. For the case of no real depth label， it can better learn the depth features and effectively solve the problem of human pose recognition with occlusion. In human 3.6m and mpii data sets， the average error of joint point prediction is lower than that of other methods， and has better 3D human posture recognition effect.

A heat pump air conditioning system performance simulation platform for electric vehicle is designed and built, which can be used to analyze the system performance in different working modes. The experimental results are used to verify the simulation model accuracy. The results show that the maximum error between experiment and simulation data of compressor power, heat exchange rate and system Coefficient of Performance (COP) is 4%~10.09% in different working modes. The compressor power gradually increases, the heat exchange rate gradually increases and the system COP gradually decreases with the increase of compressor speed in the cooling mode and heating mode. The influence of inlet air flow rate, inlet air temperature, inlet air mode and other factors of condenser and evaporator on the system performance in the cooling or heating mode are also researched in the paper. The results indicate that high air flow rate will improve the cooling capacity, and the effect of inlet air temperature is dependent on working condition, while the partial inlet air mode can save energy in heating mode.

In the unsupervised domain adaptive transfer learning process， domain-independent features lead to the degradation of model segmentation performance， but there is no effective feature selection method for transfer learning segmentation model at present. To solve this problem， a general feature selection module for transfer learning was proposed based on optimal transport， which can be applied to various unsupervised domain adaptive image segmentation models. In this module， the optimal sample subsets of two domains are selected by weighted optimal transport of segmentation accuracy， and then the features of sample subsets are subjected to entropy regularized optimal transport， so as to obtain a descending list of similarity between two domains to remove domain-independent features. The universal feature selection module is applied to three unsupervised domain adaptive models to solve the problem of Covid-19 image segmentation， which improves the model performance to a certain extent.

Flatness is an important shape deviation. A flatness error measurement method was proposed based on line structured light vision technology for flatness measurement in the field of mechanical component manufacturing and processing. Firstly， the images of light strips were collected， and the spatial coordinates of the scanning points were obtained by the corresponding light plane equation for each position. Secondly， the evaluation methods for flatness errors in national standards was analyzed and a measurement algorithm for flatness errors was established based on geometric constraints. Finally， through the proposed method， the evaluation base plane and flatness errors were calculated using the spatial coordinates of the scanning points. In the experiment， the positioning surfaces of the insert molds are selected as the measured planes， and the measurement results obtained by visual measurement are compared with those obtained by the contact measurement method. The measurement error is less than 20 μm. The experimental results show that the measurement method proposed is feasible and improves the measurement efficiency of flatness error.

In order to manual localization and quantitative analysis of stroke lesions is time-consuming and lacks consistency. This paper proposes a multi-scale U-Net deep network method to segment high density sign of ischemic stroke from non-enhanced Computed Tomography (CT), and use the Dice loss to train the model to combat the class imbalance in the image. Experiments show that the model can automatically learn salient features of high density sign in an end-to-end data-driven manner, effectively segmenting small lesions.

Fault diagnosis technology can be used to detect the potential fault of the equipment by analyzing and detecting the signal, so as to ensure the operation safety and effectively improve the operation efficiency of the equipment. The bearings are widely used in rotating machinery and equipment. The fault of the bearings may seriously affect the normal operation of equipment, and inflict economic damage, even endanger the safety of staff. Therefore, it is of great theoretical and practical significance to monitor the health status of the bearing, find out the fault location and analyze its severity in time. In the actual engineering conditions, the operating environment and workspace of mechanical equipment are characterized by complexity and variability. The intelligent fault diagnosis method based on Artificial Neural Network (ANN) can effectively identify the health status of equipment, but the traditional ANN requires a large number of labeled samples for training, which greatly limits its application in equipment fault diagnosis. Also its adaptability to different working conditions is poor. In order to solve this problem, this article proposed a model of bearing fault diagnosis based on transfer learning theory. The model consists of stacked sparse AutoEncoder (SAE) and flexible maximum function (Softmax) regression. In this model, high order KL divergence (HKL) is used to train domain adaptive ability, which can transfer the working condition with a large number of known data to the similar condition with a small amount of data. Only a small amount of data is needed to train the model to adapt to the new working condition. The experimental data set of bearing from Case Western Reserve University was used to verify the effectiveness of the model.