Vanadium redox flow battery （VRFB） has a brilliant future in the field of large energy storage system （EES） due to its characteristics including fast response speed， large energy storage capacity， low cost， high efficiency， long service life and low pollution. Although vanadium redox flow batteries have been widely used in commercial applications， their energy density and efficiency are limited by electrode activity， temperature stability， cross contamination， and voltage loss. This review， which includes the four aspects of electrode， electrolyte， membrane， and bipolar plate on VRFB， mainly illustrates the working principle of VRFB technology， while introducing the different components of VRFB and their latest research status， summarizing the limitations of current VRFBs， and discussing the solutions to these limitations， so as to highlight the development direction of VRFB technology.

Based on the research progress at home and abroad， the research status of automobile wheel hub fixtures is reviewed from two aspects： fixture structure design and fixture performance analysis. In terms of fixture structure design， it is pointed out that the traditional automobile wheel hub fixture structure is not flexible or has a low degree of flexibility. In terms of fixture performance， it is analyzed from the loss of clamping force， fixture positioning error， fixture component vibration and noise. The current research status of automobile wheel clamps is discussed in five aspects of clamping stiffness and clamping force detection technology， and the deficiencies of existing research are pointed out. Corresponding analysis is made for these problems， and the future development trend of automobile wheel hub fixtures and issues worthy of attention are prospected.

Based on the homogeneous charge induced ignition combustion mode， this paper studies the effects of gasoline ratios and diesel injection timing on combustion conventional gases and particulates emission. The engine speed is set to 1600 r/min， the total calorific value of the circulating fuel injection quantity is set to the total calorific value of the original engine under 50% working condition and the gasoline ratio is set to 65%， 70%， 75%. The injection time is fixed at -340°CA； gasoline injection pressure is fixed at 0.35 MPa， diesel main injection time is -10° CA， -5° CA and 0° CA， diesel pre-injection amount is 30% of total diesel injection amount， The results show that under the optimal strategy in this test， the torque is increased by 5%， the specific NO_x emission is reduced by 60% to 0.31 g/（kW·h）， the concentration of nuclear particles is reduced by 80%， and the concentration of accumulated particles is reduced by 97%， The research results of this paper show the great potential of homogeneous mixture pilot mode in reducing particulates and NO_x.

Traditional probabilistic methods require large number of samples to determine the probability density distribution of the dependent variable when analyzing the fatigue life of structures， and a small error in the probability density function will cause a large error in the fatigue life of the structure. To solve this problem， an interval analysis model for structural fatigue life under random loads were proposed. The uncertain factors that affect the fatigue life of the structure were regarded as interval variables. The dimensionality reduction expression of the fatigue life of the structure under random load was given. Combined with the interval mathematical method， the upper and lower limits of the fatigue life of the structure were obtained. Numerical examples show that compared with the Taylor method， the structural fatigue life interval analysis method based on dimensionality reduction algorithm proposed in this paper has higher accuracy. For the strength limit and fatigue parameters， the first-order dimensionality reduction algorithm has almost the same accuracy compared with the second-order Taylor method. For fatigue loads， when the variation range is large， the Taylor method cannot meet the accuracy requirements， but the second-order dimensionality reduction algorithm is still steady. It has high calculation accuracy and stability. Its calculation accuracy is roughly equivalent to genetic algorithm within a certain range， but the calculation efficiency is much higher than that of the genetic algorithm.

By studying a large number of historical data of high-speed train bearing temperature， the opinion， which the trend among related measuring positions of bearing temperature are similar， was used to identify the state of bearing temperature. Firstly the unsupervised learning algorithm One-class SVM was applied to abnormality detection. The result shows that the model could detect the abnormality well but not identify the state consistently. Then the whole process， which meant the bearing worked from normal to failure according to the relative temperature， was divided into four phases， the long， medium， short and urgent phases. The way， which describes the trend of bearing temperature through image including the information of relative temperature and the changing rate of temperature， was proposed. Finally， the deep learning algorithm CNN was used to identify the bearing temperature state consistently. The result shows that the CNN model could reliably identify the state of bearing temperature， and this could provide some support for predictive maintenance.

In order to evaluate the influence of fault propagation of machining center components， identify the key components of the system quickly and accurately， and control the fault propagation effectively， the fault propagation path between components is characterized by the combination of fault propagation mechanism analysis and directed graph. DWNodeRank algorithm is used to evaluate the fault influence degree among components， and the fault propagation rate of components was calculated based on the failure rate index. Considering the effective reachable path， the influence force value of fault propagation is calculated based on the improved ASP algorithm， and the fault propagation influence is evaluated. An example analysis results show that the DWNodeRank algorithm can reduce iteration complexity effectively and evaluate node influence degree accurately considering the direction and intensity of fault propagation. The fault propagation rate is used as the basis for evaluating the influence of fault propagation， and its dynamic time-varying characteristics make the evaluation results real-time and accurate， which is of great significance to the health maintenance of machining center.

In view of the influence of temperature change on steel-aluminum clinch-adhesive connection structure during the coating process， the main factors influencing the thermal stress were studied. The high temperature thermal expansion tests were carried out based on the tensile tests and thermal expansion tests of steel， aluminum and adhesive， and the strain value was obtained. The finite element simulation model of thermal expansion of steel-aluminum clinch-adhesive connection structure was established， which was verified by experiments. The stress changes of the joints and the bonding part in the process of heating， heat preservation and cooling were analyzed， and the variation of the overall size of the steel-aluminum clinch adhesive connection structure with temperature in the thickness direction was obtained. The effects of adhesive thickness， bonding width and riveting sequence of metals on the properties of steel-aluminum clinch-adhesive connection structure were studied. The results show that the residual thermal stress and thermal deformation of the structure occur after high temperature. Residual thermal stress and stress concentration and thermal deformation of the whole structure can be reduced by properly increasing the thickness of the adhesive. Increasing the proportion of adhesive width can effectively reduce the residual stress in the joint， but it leads to the increase of plastic deformation in the steel-aluminum clinch-adhesive connection structure. The residual thermal stress of clinch-adhesive joint is smaller when aluminum plate is riveted at the top to steel plate.

It is found that the assembly gap is inevitable in the welding process of the car body，especially in the manufacturing process of the large-size components such as the side walls of the vehicle body. In general，the existence of assembly gap will lead to quality problems，such as lack of fusion and surface forming，which is harmful to the static strength and fatigue strength of the weld. In this paper，laser welding was used to remelt and repair the surface collapse defect of laser weld，and the effects of laser power，welding speed and focusing distance on the surface quality and mechanical properties of repaired welds were studied. Furthermore，the orthogonal optimization test method was used to optimize the laser repair process parameters. The optimized parameters of laser repair process are as follows： laser power is 1.6 kW，welding speed is 3.81 m/min and focusing amount is 0 mm. The maximum tensile shear force of laser repaired weld is 16.3 kN，which is greater than that of the laser weld without defects，and the crystal grains of the laser repaired joint are not coarsened. The research shows that the laser repair technology can effectively repair the surface collapse of the laser weld and the tensile shear force of the repair weld can meet the technical requirements of the actual production. This technology can be applied to the actual production.

The buckling performance and imperfection sensitivity of variable stiffness cylindrical shells based on automatic placement technology are studied. Based on the tow shearing/refeeding technology， a simple algorithm that can accurately simulate the overlapping or defect area of the cylindrical shell surface is proposed to make the finite element model closer to the actual structure. A study was conducted on the effect of curved fiber layup on the buckling performance of cylindrical shells under combined loads. Combined with the Kriging model and genetic algorithm， the layup design method， in which the fiber angle changes linearly along the hoop direction and the axial direction， is optimized. The modified arc length method was used to analyze the imperfection sensitivity of composite stiffened cylindrical shells with different mode imperfections. The results show that under the combined action of axial pressure and uniform external pressure， the layering method， in which the fiber angle changes along the axial direction， can effectively improve the buckling performance of the structure. When the bandwidth is 25.4 mm，T₀=80，T₁=45， the buckling performance of the variable stiffness cylindrical shell is optimal， and the critical buckling load is increased by 70.9% and 34.5% compared to the reference configuration and to the constant stiffness optimal configuration. The imperfection sensitivity of the variable stiffness cylindrical shell is also improved compared to the quasi-isotropic layering method and to the optimal constant stiffness layering method. Variable stiffness design can make the cylindrical shell structure under combined load improve the structural buckling resistance and reduce the sensitivity to geometric imperfections.

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.

Through analysis of the real bus operating environment， considering the difference in operating characteristics between working days and non-working days， a new bus arrival time predicting method was proposed based on the beetle Wavelet neural network （BAS-WNN） prediction model of search algorithm. This model uses the beetle whisker search algorithm with stronger optimization performance to optimize the initial parameters of the WNN so that the WNN has better performance in the prediction of time series. Finally， the historical driving data was used to train and model the neural network to achieve accurate prediction of the arrival time. The optimization algorithm is simulated with the traditional WNN algorithm and the Elman neural network algorithm in MATLAB. The comparison results show that no matter the working day or on non-working days， the BAS-WNN prediction model has a higher accuracy in predicting bus arrival time and the results are more stable.

In order to enable the occupants to carry out targeted protection according to different locations when the bus rear end collision accident occurs， the bus model， 12 dummy models and seat belt models were constructed by using the finite element analysis and rigid multi-body dynamics software. The dummy models were assigned to different seats of the bus. The gravity acceleration， impact waveform and relative impact velocity were loaded into the simulation model. The injury indexes of the dummy head， chest and leg under different relative impact velocities were finally obtained. The results show that the larger the relative impact speed is， the greater the injury index values of head， chest and leg are. When the relative collision speed is greater than 60 km/h， some occupants' head and chest in the bus will suffer serious injury of grade 4 or above. The weighted injury criteria values （WICs） of the occupants on the front and rear seats are larger than that of the occupants on the middle seat. The WIC of the occupants near the windshield side is larger than that of the occupants near the aisle side of the same row of seats.

The prediction of traffic operation risk state level after traffic accident is the technical support for real-time risk control and emergency treatment. Based on the accident data， this paper studies the post event risk state， and establishes a traffic operation risk level prediction model based on the improved deep forest algorithm. Firstly， we analyze the importance of accident characteristics， establish the accident feature screening mechanism based on extreme gradient boosting algorithm， introduce Bayes parameter optimization and ten-fold cross validation method to realize the super parameter optimization of deep forest model. Then， we design the cyclic multi granularity scanning method and weighted cascade forest structure， obtain the mapping relationship between the traffic operation risk status level and the accident characteristics， and propose the traffic operation risk level prediction method based on the improved deep forest model. In order to verify the effectiveness of the proposed method， it is compared with SVM， RF and other methods. The experimental results show that the prediction accuracy of the model is 90.80%， and the auc value is 0.99， which show good prediction performance and generalization ability. Compared with the control experiment， the proposed model has obvious advantages. In the actual effect test， it also achieved good prediction effect.

With the increase of electric vehicle mileage and the increase of public charging facilities， it is inevitable that electric vehicles choose to charge in their commute more and more widely. Considering the stochastic charging demand of electric vehicle users， a mixed user equilibrium model is constructed based on the stochastic charging behavior and charging queuing simulation. Under different initial state of charge and market share of electric vehicles， the trend of equilibrium traffic flow and potential charging demand flow is predicted. Based on stochastic charging behavior and charging queuing simulation， a mixed user equilibrium model is constructed. The model is based on Frank?Wolfe algorithm and multi-label algorithm. Taking Nguyen-Dupius network as an example， this paper designs an equilibrium traffic flow prediction model integrating electric vehicle charging queuing simulation， which provides a scientific management scheme for traffic managers.

To reasonably predict serviceability-based dynamic reliability of the long-span bridge girder， using the dynamic extreme deflection data at the control monitoring points， a dynamic serviceability reliability prediction approach for the long-span bridge girder is presented through the fusion of multi-dimensional Gaussian copula technique， multivariate Bayesian dynamic linear models （MBDLM） and the First Order Second Moment（FOSM） method. In this approach， the nonlinear correlations among the performance functions for deformation failure modes at the multiple control monitoring points are taken into consideration. The dynamic monitoring extreme deflection data at three control monitoring points from a long-span bridge girder was provided to illustrate the proposed models and methods. The results show that the predicted dynamic reliability of the bridge girder， considering the time-variant nonlinear correlation of deformation failure modes at the multiple control monitoring points， is larger than that without considering the time-dependent nonlinear correlation of deformation failure modes. This suggests that the predicted results without considering the time-variant nonlinear correlation of deformation failure modes at the multiple control monitoring points are conservative.

In this paper， we focus on completing the missing domain and range constraints in knowledge graphs and try to predict missing constraints by knowledge graph embedding models. Considering the structure of constraints completion problem， we introduce two efficient approaches， DCaT-T and DCaT-R， which are derived from translation-based knowledge graph embedding models TransE and RotatE. In particular both DCaT-T and DCaT-R exploit a two-stage training approach to improve the performance of the constraints predicting models. Experimental results show that both DCaT-T and DCaT-R are efficient than entity typing approach SDType， DCaT-T performs better than TransE-based entity typing model ConnectE and the two-stage learning approach can improve the performance of the models further.

Existing object detection methods have several problems when detecting traffic objects in uav aerial images， including the poor fittness between the horizontal bounding box and the rotated objects， incorrect suppression due to the high overlap between the bounding boxes， and the mismatch between the sampling points of the standard 2d convolution and the rotated objects.To solve these problems， a single-stage rotated object detection network called ATB-YOLO based on YOLOv3 was proposed. For the backbone of the network， a new feature extraction network called Darknet-53-Dense was designed. The Mish activation function was used to replace the Leaky ReLU activation function in Darknet-53， and the concatenated blocks were used to replace the residual blocks refering to the DenseNet. In the detection head， an Anchor Transformation Net （ATN） was proposed to transforms the initial horizontal anchors into rotated ones. An Anchor Aligned Convolution （AAC） was proposed to adjust the sampling position of convolution operation under the guidance of the rotated anchors. The extracted aligned features were then used to predict the final rotated bounding box and the category of the objects. Experimental results show that the proposed backbone improved the detection accuracy by 1.38%. The proposed AAC feature improved the accuracy by 4.38%， 4.24% and 3.79% respectively compared with the stantard convolution， the deformable convolution and the guided anchoring deformable convolution. Compared with several recent rotated object detection networks， the proposed method can do the detection at a framerate of 21.2 fps while achieving a competitive accuracy as the two-stage detector.

This paper presents a reliable digital watermarking technique. For the first time， the watermark image is preprocessed by halftone and quad tree techniques， and the position information of the content is extracted as the actual embedded value. The cover image is processed by Discrete Wavelet Transform （DWT）， and then the high frequency sub-band is divided into blocks， sized by . The blocks were processed by an optimal Discrete Cosine Transform （DCT） psychovisual threshold. Embedding process in this paper utilizes certain frequency regions of DCT such that insertion of watermark bits causes the least image distortion. The information was extracted by comparing certain coefficients based on some rules. According to the information， the watermark image can be reconstructed. The experimental results show that our algorithm achieves higher capacity and robustness than the existing schemes. The watermark extraction produces high image quality after cutting， noise， filter and JPEG compression attacks.

In this paper， a real-time mosaicing and visualization method for phased-array 3D acoustic topography is proposed. The translation and rotation matrix of sonar transducer carrier are obtained by differential GPS and attitude instrument as the initial iteration parameter， which eliminates the iteration time-consuming and mismatch problem. The obtained registration matrix converts the two adjacent acoustic images into the same coordinate system. Then the reference grid is used to rasterize the acoustic images to obtain cross point. In order to generate compatible data sets for surface reconstruction and reduce the computational complexity of mosaicking and reconstruction， the peak redundancy points in acoustic images are deleted. In order to accelerate mosaicing speed， the system adopts multi-thread parallel processing and GPU 3D image rendering acceleration architecture to balance the load between multiple CPU cores， and then uses GPU and VTK library to accelerate 3D image rendering. The results of indoor pool and lake experiments show that the method can effectively realize the real-time mosaicing and visualization of 3D acoustic topography.

Accurately identifying protein-ATP binding site is important for the research of protein function and the disease drug design. In order to improve the prediction accuracy of identifying protein-ATP binding site， first， a deep neural network model， Inceptiobase， based on inception architecture was proposed. Then， the network model and training strategy were optimized and improved through large number of experimental tests， and an upgraded deep neural network model， Inception_evolution， was proposed. Through two sets of data sets tested on the model， the AUCs are 0.885 and 0.918， respectively， which are better than other comparative machine learning methods. The experimental results show that the deep learning method can be applied to the protein-ATP binding site prediction problem， and the model Inception_evolution can predict the protein-ATP binding site more accurately.

Aiming at the problems of noise sensitivity and long training time in the uplink interference classification model of LTE （long Term Evolution） network， this article establishes a LTE network uplink interference analysis model combining stacked denoising autoencoder and extreme learning machine， using uplink interference raw data to unsupervised pre-training of SDA （Stacked denoising autoencoder） to extract high-level abstract features， and to provide initial parameters for the ELM （Extreme learning machine） classifier. The model takes advantage of ELM's fast convergence and SDA's noise suppression， and at the same time overcomes the problem of insufficient robustness caused by random assignment of ELM parameters. Experimental results show that this model improves the efficiency of LTE network uplink interference analysis， and at the same time has strong robustness.

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.

To overcome the high complexity of large-scale multiple-input multiple-output orthogonal frequency division multiplexing systems， a low-complexity Wiener filter channel estimation algorithm is proposed. Firstly， the large-dimensional in-situ replacement method is applied to the minimum mean square error algorithm. It can effectively reduce the computational complexity of multiple antennas. Then， to reduce the complexity， the two-dimensional Wiener filter is splited into two one-dimensional Wiener filters in the time domain and the frequency domain. At the same time， in order to reduce the estimation error caused by the difference of the two Wiener filter noise variances， the median value of the noise variance is introduced for correction. The simulation results show that compared with many traditional algorithms， the computational complexity of the proposed algorithm is reduced by at least 20%， and the bit error rate is reduced by at least 9%.

To solve the problems of high grain loss and high impurity rate in wheat combine harvester cleaning operation in field harvest， the optimization experiment of wheat combine harvester cleaning operation parameters was carried out to explore the influence rules of operating parameters of the whole machine's cleaning device on the cleaning loss rate and impurity rate in grains， and the optimal combination of cleaning operation parameters was obtained. Test model is determined based on the airflow movement analysis， and the factors， the amplitude， frequency of cleaning sieve， fan wind speed and air flow direction angle of four parameters for the single factor tests and response surface regression test. The results show that the fan speed is significantly influence factor affecting the grain impurity rate， amplitude and frequency of cleaning are the significant influencing factors of loss rate. Using response surface test method and design-expert， the regression mathematical models of cleaning loss rate and impurity rate of grains were established， and the optimal operating parameters were obtained： the cleaning screen amplitude was 31 mm， the cleaning screen frequency was 4 Hz， the fan wind speed was 12 m/s， and the airflow direction angle was 26°. The experimental results of various parameter combinations are as follows： the rate of impurity in grains is 0.476%， and the rate of cleaning loss is 0.438%. The rate of impurity in grains and the rate of cleaning loss are 0.439% and 0.410% respectively， and the relative errors between them and the theoretical calculation are 7.8% and 6.4% respectively.

To remediate the degraded grassland， the root-cutter with fertilization and reseeding compound machine was designed， which could realize the multi-functions of root-cutting with fertilizing， root-cutting with reseeding and root-cutting with fertilizing and reseeding. This machine can be used to solve the problems in furrowing and cutting process， such as the high firmness and great amount of the stubble of the grassland soil， the unevenness of seeding rate caused by the sliding of the wheel on the uneven grassland with many holes and gravel. The edge curves of the root-cutting blades were optimized， showing that the exponential sine curve was the best suitable edge curve when the value of the beginning static sliding cut angle was 40° and the proportional coefficient between the increment of the static sliding cutting angle and the polar angle was -0.1. The layered- staggered- covering furrowing device was innovated. the working process includes the following steps， after the soil surface was broken by the stubble-cutting blade， seed bed was formed with seeding double disc furrow opener， fertilizer bed was formed with fertilization double disc furrow opener pressing down the soil to the specify depth in the root-cutting opening and pushing the soil to cover the seed. The results show that the device can furrow stably and form suitable fertilizer bed and seed bed. The depth and width of the fertilizer bed were 47.3 mm and 29.5 mm， the depth and width of the seed bed were 44.7 mm and 19.9 mm， while the soil broken rate was just 14.2%， which benefits for the protection of the grassland degradation. The seeding control system of the groove wheel was designed by controlling the rotation speed and the working length of the groove wheel seeder. In the system， the two signals of the two ground wheels speed were collected and compared， the higher one was used for the control unit to control the speed of motors to change the rotation speed length of the groove wheel seeder to keep the seeding amount consistent by synchronizing the speed of the above two motors with the speed of the tractor. The simulation through the Protues software showed that the system could compare the two signals of the ground wheels and keep the rotation speed of the groove wheel seeder and the ground wheel synchrony and change the transmission ratio. It was helpful to improve the uniformity of seeding rate.

To realize the information traceability of the whole process of silage production， this paper developed a mobile Internet of Things information acquisition terminal based on digital agricultural machinery equipment and a ZigBee-based silage information monitoring terminal. The multi-sensors were used to collect the sowing amount， fertilizer amount， drug amount and plant growth amount in the planting process. Also the temperature and humidity， oxygen concentration， carbon dioxide concentration and pH value were collected in the storage process. The double terminals were planted at the bottom of the silage traceability platform. Combined with HACCP system， the whole process of information collection， monitoring and early warning of fermentation information of silage from planting site to the silage cellar was realized. The platform is developed based on B/S architecture， using Java language to develop on the.NET framework. The database was SQL Server 2016，and the Servers were Ali Cloud 2 core 4G and Tencent Cloud 2 core 4G. Using QR code as label information， the whole process information of silage production supply chain can be traced. At the same time，the system has a quality and safety warning mechanism to ensure the safety and reliability of silage.The traceability platform based on WeChat applet was developed under the unified database，which expanded the way of consumer traceability.The system was tested in a silage production enterprise in Jilin Province， and the results show that the system could effectively collect the information of silage corn planting process and silage system in the silo，the nutrient loss rate was reduced to 8%~10%.