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.

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 develop alkylpolyglycoside(APG)which is a highpurity and environment friendly biosurfactant,the processparameters of APG synthesis catalyzed by ACA microcapsuleim mobilized enzyme were optimized through single factor experimentation and twice or thogonalrotating combination igntest,andthe model of glucose conversion rate was studied.The results show that factors which have influence onglucose conversion rate are:reaction time,amount of adding water,reaction temperature and amount of alcohol,what’s more,the formeris better than the latter;reaction temperature is more interactive with a mount of alcohol,amount of adding water,reaction time,a mount of alcohol and amount of adding water is the same;the best synthesis process parameters are as follows:reaction temperature at 45 ℃,amount of dodecanol of 8 mL,immobilized enzyme of 5 g,amount of glucose of 1 g,without adding water,reaction time of 5days and the maximum of glucose conversionrate is97.9%.

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.

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 response to the problem of vibration caused by the large length/diameter ratio of the boring bar during deep hole boring， which affects the processing quality and efficiency， three vibration control methods， passive control， semi-active control， and active control， were summarized. The specific structures， vibration reduction mechanisms， characteristics， shortcomings， and development trends of the three methods have been sorted out. Comprehensive analysis shows that the structure， materials， and control methods of vibration damping boring bars are currently the focus of research. With the continuous development of structural design， material science， vibration reduction mechanism， control theory， big data， artificial intelligence and other technologies， the research on vibration damping boring bars is gradually becoming diversified， integrated， and intelligent. Meanwhile， intelligence is a new development direction for vibration damping boring bars.

A survey of the mainstream multi object tracking methods based on tracking by detection in recent years is carried out. Then， the concept of detection based multi object tracking is introduced. The multi object tracking methods are summarized in object detection， feature extraction and data association & tracking. The performance of some multi object tracking（MOT） methods are compared and evaluated on the MOTChallenge series datasets. The future development direction of multi object tracking is discussed.

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.

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 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.

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.

Aiming at the problem that ground points in point cloud data can affect the precision and speed of environment perception， an accurate segmentation method of ground point cloud based on plane fitting was proposed. Firstly， the scene point cloud was divided into several areas based on the projection distance. Secondly， according to the average height in the area， the divided ground points and the normal vector direction of the ground plane， the ground plane fitting point was determined， and the ground plane was fitted. Finally， according the distance from the point to the ground plane to achieve ground segmentation. Using the KITTI dataset and the collected point cloud data to compare the proposed algorithm with four algorithms： RANSAC、GPF、R-GPF and PatchWork， verify the effectiveness of area division， fitting point screening and ground plane normal vector direction screening for ground segmentation. The experimental results show that after the area division， the far-distance sparse ground can be divided； after the fitting point screening， the ground segmentation accuracy reaches 0.9417 under the condition of low iteration. After screening the normal vector direction of the ground plane， fitting the wall to the ground was avoided. The proposed method is better than the four compared algorithms in terms of F₁ score， recall rate and accuracy rate， and the speed can reach 42.78 Hz， which can divide the ground accurately and quickly.

To solve the problem of road roughness identification, a NARX neural network identification method and its applicability are studied based on vehicle responses. A four degree of freedom plane model of vehicle vibration system is established, thus, the vehicle responses and road roughness of wheel can be obtained by simulation. The application selection, input scheme optimization and evaluation index of NARX neural network are studied, and the solutions of vehicle response selection and its combination optimization are put forward. The NARX neural network is used to identify the road roughness at front wheel of a car under the common road grade B and 60 km/h driving speed, for which the correlation coefficient and root mean square error are 96.75% and 0.003 3, respectively. The influences of training sampling points, vehicle response random noise, vehicle speed, and road grade on the NARX neural network are considered, and the adaptability of NARX neural network method for road roughness identification based on vehicle responses is illustrated. The results show that the use of orthogonal test design to determine the optimal input scheme of the NARX neural network and the identification of road roughness based on vehicle responses can achieve satisfactory performance and good applicability.

CNC machine tools play an important role in the equipment manufacturing industry， and their reliability level has become the bottleneck restricting the development of the industry. Reliability modeling of NC machine tools is the basis of reliability engineering. A comprehensive review on the state of the reliability modeling technology research is given. Reliability models are mainly divided into four categories： reliability modeling method based on fault time data， reliability modeling method based on multi-source hierarchical information set， reliability modeling method based on performance degradation data and process reliability modeling method based on dynamic characteristic parameters. The research process and technical progress of various modeling methods are analyzed. On the basis of affirming the obvious progress made in the reliability modeling method and technology of CNC machine tools， this paper analyzes and points out the existing problems and shortcomings of the research work， and discusses the trends and hotspots of the reliability modeling research of CNC machine tools. Finally， the development trend of reliability modeling methods and technology of CNC machine tools is prospected from the perspective of reliability modeling development law、engineering application and industry demand.

As a new generation of automotive materials， smart materials provide new research directions and design ideas for the realization of automotive lightweight and intelligent design goals. In order to further promote the research process of smart materials for automobiles， this paper reviews piezoelectric materials， magnetorheological materials and shape memory alloys respectively， describes the special properties of various materials， and systematically summarizes domestic and foreign research achievements in important research fields such as automotive energy recovery， structural vibration suppression， sensors， actuators， and safety protection. Finally， the challenges of commercialization of smart materials for automobiles are analyzed， and the directions of future research are pointed out.

In view of the current high-power fuel cell system, a high-power fuel cell test platform was designed and developed, and the performance test of the 120 kW fuel cell was completed through the design, selection, construction, and debugging of the system. The test platform integrates hydrogen flow， air flow and thermal management control systems, integrates and optimizes the layout between the various subsystems, and makes post-maintenance more convenient.At the same time, the test platform uses LABVIEW software to design the control interface of the upper computer, uses Simulink software to design the control program of the lower computer controller and writes the determined parameters of the components into the controller, and then realizes the communication through the CAN box between the upper computer and the controller. The online real-time control of the system by the host computer control interface, and can automatically optimize the operating parameters of each component according to the load change. By analyzing the test data collected by the test platform, it is possible to evaluate whether the fuel cell system meets the expected design requirements. The test platform has certain guiding significance for the research and production of high-power fuel cell systems and the development of fuel cell test platforms, and provides a guarantee for the development of fuel cell systems.

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.

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.

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 new method for Chinese named entity recognition at word level based on transformer and hidden Markov model is proposed. The position coding calculation function of transformer model is improved， so that the modified position coding function can express the relative position information and directivity between characters. The character sequence encoded by transformer model is used to calculate the transfer matrix and emission matrix， and a hidden Markov model is established to generate a group of named entity soft labels. The soft label generated by hidden Markov model is brought into Bert-NER model， the divergence loss function is used to update the parameters of Bert-NER model， and the final named entity strong label is output to find the named entity. Through comparative experiments， the F₁ value of the proposed method in Chinese cluster-2020 data set and Weibo data set reaches 75.11% and 68%， which improves the effect of Chinese named entity recognition.

Aiming at driver panic or other factors causing the lack of braking force and decreasing the driver's braking operating burden when driving on a long ramp, HBA and HDC control strategy is carried out by Stateflow state machine theory and Matlab/Simulink. The brake pedal displacement and speed are selected as the identification parameters to better identify the driver intention. Meanwhile, the ramp recognition is designed by Newton's second law. Considering the braking problem of high temperature failure, the temperature model is built. Based on the above, MIL and HIL are carried out, the results verify the correctness of the control strategy. In addition, road tests' results show the HBA function can meet the ECE standard and the HDC function can keep vehicle speed near the target speed, proving that the control strategy achieved good control effect.

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.

This paper proposed a COVID-19 chest CT image segmentation method based on Federated Learning (FL) and blockchain to automatically segment the area in lung affected by COVID-19. Firstly, in the situation where the sample data of patients is limited and it is distributed in different institutions, which cannot be easily collected, we applied FL method. Then, we used blockchain network to replace the central server in FL to solve the “single point of failure” problem. Finally, we designed a lightweight separable convolution U-NET to reduce the cost of computation and time. Experimental results show that the method has good performance after training, and its dice metric can achieve 63.26%, which is helpful for diagnosis of COVID-19.

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.

Based on the application scenarios of swarm intelligence in the field of UAVs， the application of swarm intelligence methods in the field of UAVs was reviewed. First， the recent application status of UAVs was reviewed， and the principles of swarm intelligence algorithms and examples of UAV applications were introduced. Second， the application scenarios of swarm intelligence in UAVs were divided into four parts： swarm intelligence-based UAV wireless communication， swarm intelligence-based UAV ad hoc network， swarm intelligence-based UAV trajectory planning， and swarm intelligence-based UAV intelligent decision-making. The progress of relevant research work for each part is introduced separately. Finally， a brief discussion is conducted on the development trend of swarm intelligence for UAVs.

In order to accurately and reasonably predict the future trajectories of vehicles and understand the changes of surrounding traffic flow, a trajectory prediction method combined with high definition map in graph attention mode was proposed. The encoder-decoder framework based on LSTM network was designed, and the model structure with vehicle historical status and high-precision map information as input was established. A graph query mechanism combining local and global features of vehicles was proposed to output vehicle prediction trajectory. The results of experiments carried out on the nuScenes dataset show that the comprehensive prediction performance of our model is better than other state-of-the-art methods， such as Traj++， CoverNet， etc.， and it has good anti-interference.

A long-term and short-term memory neural network （LSTM） based on particle swarm optimization （PSO） was proposed to predict the life of PEMFC. First， the degradation mechanism of PEMFC was analyzed. Then， the voltage degradation prediction model was established by using LSTM neural network and the Dropout layer was used to prevent overfitting to improve the generalization ability of the model. In addition， PSO was used to optimize the learning rate and Dropout rate in LSTM to improve the prediction effect. Finally,the actual aging data of IEEE 2014 Data Challenge Data fuel cell were used to verify. The results show that this method can accurately predict the degradation of fuel cells， and the prediction accuracy is improved by 50% compared with the traditional LSTM.

In order to prolong proton exchange membrane fuel cell （PEMFC） lifespan， it is necessary to design a state observer to monitor the internal states， and control the internal states at the expected level through feedback control. Since the anode hydrogen concentration directly determines the output performance of PEMFC， and circulation of anode hydrogen as well as nitrogen accumulation caused by diffusion across membrane leads to the difficulty of anode gas concentration estimation. Therefore， this paper focuses on the cutting-edge technology of PEMFC anode gas concentration estimation， and the existing problems as well as the future development trend of existing research are also described， hoping to make contributions to the research of gas， water and heat management for PEMFC.

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.

For two lane traffic scenarios， a decision-making and optimization-based lane changing trajectory planning method for autonomous vehicles was proposed. Firstly， a risk assessment method based on the Bayesian probability theory was designed to obtain the conditional probability of the lane safety in the current scenario； then， a behavior decision-making method based on the safety utility was designed. According to the risk assessment Bayesian network and decision graph， the behavior decision of lane keeping or lane changing was made. An optimization-based trajectory planning method based on the nonlinear MPC was proposed at the trajectory planning layer， which imitates the excellent driver to give the weight coefficient of each optimized objective function to solve the optimal desired lane changing trajectory. At last，The effectiveness of the decision-making and trajectory planning method was verified by the simulation. The simulation results show that the risk assessment， behavior decision-making and optimization-based trajectory planning method can make the safe behavior decision and plan the optimal lane changing trajectory for autonomous vehicles in different risk scenarios， so that the autonomous vehicle can change the lane safely and quickly.

In view of the deficiency of existing research on analyzing the resilience of urban rail transit network based on topological network efficiency， a resilience assessment method based on network performance response function is proposed， which is the weighted sum of OD passenger flow loss ratio and network service efficiency loss ratio. The Delphi-entropy weigh method is used to determine the comprehensive weight of two indicators， and a recovery optimization model with the maximum network resilience index is established， and the adaptive genetic algorithm is adopted to solve the developed model. Taking Xi'an rail transit network as an example， four hypothetical perturbance scenarios are proposed considering random attack and intentional attack. The differences of network resilience repair effects of target recovery strategy， random recovery strategy and preference recovery strategy under four hypothetical perturbance scenarios are compared and analyzed. The results show that the target recovery strategy has the best repair effect on rail transit network， followed by preference recovery strategy. Compared with random attack strategy， different recovery strategies have different network resilience under intentional attack strategy. When selecting the sequence of repairing damaged stations， we should not only consider the importance of damaged stations in network topology， but also consider the impact of passenger flow on network performance. Increasing the input of repair resources can shorten the recovery time and improve the repair efficiency， but the increase of repair resources is not proportional to the improvement of network resilience. The research conclusion can provide decision-making basis for the resilience assessment and emergency repair recovery of urban rail transit network.

A distributed adaptive sliding mode time-varying formation control scheme based on neural network state observer was proposed to solve the problem of unmeasurable information of multi-quadrotor unmanned aerial vehicles （UAVs） system in directed switching communication network. Firstly， by designing a distributed time-varying formation control protocol， UAVS can communicate with each other only by the status information of their neighbors， which is free from the dependence on global information. Due to the underdrive characteristic， a position-assisted controller was designed to solve the target information of two attitude angles and control thrust. At the same time， a neural network state observer was designed to observe the unmeasurable information of the system， and the observed values were feedback to the adaptive sliding mode controller in real time， which improves the robustness of the UAV system. The quadrotor UAV formation system is analyzed by Lyapunov's theorem， and it proved that the multi-UAV formation error is bounded and converges to near zero. The simulation results show that the proposed control method can realize the time-varying formation control of the multi-UAV system and verify the validity of the theoretical results.

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.

Most fault diagnosis algorithms for rotating machinery are labeled and need to be set manually， an unsupervised fault diagnosis algorithm with adaptive parameters for unlabeled fault data by introducing twice anomaly recognitions and clustering algorithms was proposed.The method extracts and selects signal features by improving empirical wavelet transform and Laplace score algorithm, and adopts the unsupervised method of quadratic anomaly identification combined with improved fuzzy C-means clustering for fault identification. Through the verification of the fault data of the rotor system of the electric spindle, the diagnostic accuracy of the proposed method can reach 93%. Compared with the traditional unsupervised diagnostic method, it has good accuracy and robustness.

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.

Most of the research on text detection has been conducted on natural scene datasets， few on such specific scene. And the existing models are not good enough for text detection on traditional Chinese newspaper. In order to solve this problem， a segmentation-based text detector for traditional Chinese newspaper is proposed in this paper. The model uses Resnet50 and FPN as feature extraction network， employing a segmentation instance scaling and extension algorithm to generate the binary map for predicting text boxes. And the methods of surrounding filling and loop detection plus region coverage are proposed to enhance the detection effect. In addition， a traditional Chinese newspaper dataset is built to satisfy the research needs. The experimental results of this model on traditional Chinese newspaper dataset are around 0.9 and are improved by 5% to 7% compared with DBNet， which indicates that the model is effective and accurate for text detection on traditional Chinese newspaper.

In view of the adhesion performance between asphalt-aggregate， the research results on the adhesion and raveling mechanism， evaluation methods， influencing factors and improvement measures in domestic and overseas are summarized. Formation and failure of adhesion are complex processes involving physics， chemistry， thermodynamics， and micromechanics. The characteristics of the interface between asphalt and aggregate are influenced by the properties of asphalt and aggregate， the void and asphalt film thickness of the mixture， and the external environment. The different test systems are dedicated to the development of methods that that can not only simulate the process of damage occurrence in the field but also provide an assessment method through which the suitability of mixtures would be estimated in designing steps and would be guaranteed during the pavements service life. Combined with the existing research contents，the future research directions of asphalt-aggregate adhesion performance and asphalt mixture moisture sensitivity are prospected.

A viscoelastic 3-D finite element model was built to calculate the horizontal stresses in the surface and bottom layers of typical road asphalt pavement structure, and the effects of loading mode and temperature on their viscoelastic responses were analyzed. The results showed that the surface layer and the bottom layer of the road pavement experience the repeated horizontal tensile and compressive stresses under cyclic loading and small residual tensile and compressive stresses build up in the surface layer and bottom layer respectively. This is probably one of the factors causing top-down or bottom-up fatigue cracking in the asphalt pavement. The residual stresses, the compressive stress amplitude in the surface layer and the tensile stress complitude in the bottom layer of the asphalt pavement of the road all decline with rise of temperature.

This paper presents a 2-DOF dynamic vibration absorber to reduce the vibration of 2-DOF main system which possesses translation and rotation around the centroiding. The main system obtains a good damping effect by optimizing the stiffness and damping of the dynamic vibration absorber. The evaluation and optimization indexes are displacement, acceleration, angular displacement and angular acceleration. The nondominated sorting genetic algorithm (NSGA-Ⅱ) is used to solve the multi-objective optimization problem including four optimization objectives and three design variables. The 60 Pareto optimal solutions are ordered by entropy weight TOPSIS. The optimal solution is selected as the dynamic vibration absorber optimization design solution and a simulation analysis is carried out to obtain the frequency domain simulation results and time domain simulation results. The results show that the dynamic vibration absorber with optimized parameters greatly reduce the four vibration evaluation indexes of the main system and the vibration control effect of the two-DOF dynamic vibration absorber is good. The NSGA-Ⅱ and Entropy TOPSIS joint optimization method is suitable for optimal design of two-DOF dynamic vibration absorber.

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.