On the basis of combing and analyzing the latest research results of the extrapolation of the load spectrum of electromechanical equipment at home and abroad， this paper summarizes the research progress and comparative analysis of load extrapolation key technologies： time-domain load extrapolation， rainflow load extrapolation （parametric rainflow， non-parametric rainflow， and rainflow matrix extrapolations）， and quantile extrapolation， in view of the load form and load characteristics of the electromechanical equipment during operation. In the context of the era of big data， the potential direction and development trend of the load spectrum extrapolation method in electromechanical equipment are discussed.

The altitude pressure of 10 m， 700 m and 2400 m were simulated to explore the combustion and emission of diesel-methanol dual fuel engine in different altitude. The test conditions were 25%， 50% and 75% load at 1800 r/min. The results demonstrated that under 25% load， misfire emerge in the cylinder at 40% methanol substitution percentage （MSP） and 2400 m， which lead to the peak pressure decrease in the cylinder， the combustion delay period will be prolonged， and the diffusion combustion will be shortened. Under 50% and 75% loads， the increasing of altitude and MSP result in peak pressure increasing in the cylinder. The combustion duration decreases with the increasing of MSP and increases with the increasing of altitude in all conditions. Compared with the pure diesel mode in 10 m， under different altitudes and MSP， the maximum ignition delay period of the engine is extended by 27.01% and the minimum is shortened by 28.99%. The maximum combustion duration is shortened by 43.20% and extended by 22.79%， the CA50 moves forward by 8.55% ~ 21.82%， and the in-cylinder combustion temperature rises by 1.45% ~ 23.83%. The brake thermal efficiency of the engine decreases with the increase of altitude and increases with the increase of MSP at 50% and 75% loads. However， brake thermal efficiency presents a more complex variation rule under 25% load. Among the pollutant emission， with the increase of MSP， NO _x and Soot decreased simultaneously， HC and CO increased simultaneously under the same load. With the increase of altitude， the emission almost all showed an upward trend. The research can provide reference for the in-cylinder combustion and emission of diesel/methanol dual fuel engine in the plateau area.

To solve the problem that conventional flexible metal wheels with plate-spring simply have stress concentrations or plastic deformation， the cause of stress concentrations and large deformations in the tread is analyzed. The three-dimensional models of conventional arched tread and bilateral curved tread structure are established. And the deformation and load of the two treads are simulated under three working conditions of flat pressure， middle tread pressure and unilateral tread pressure by using finite element analysis method. The analysis shows that the restrained connection method of the ends of the tread will affect the stress situation of the tread and cause the excessive local rigidity or insufficient load capacity of the tread. According to the simulation results， the coupled flexible tread structure is proposed. The structure that restricts the ends of the tread has been changed. The radial displacement of the tread is transferred to the axial outward tension of the hub， and then the radial displacement is expanded by the axial displacement. Finite element simulation shows that the coupled flexible tread structure can obtain same greater radial deformation under all the three working conditions， and the tread has uniform rigidity， continuous deformation and no stress concentration area. The result shows that the coupled flexible tread structure can effectively expand the radial displacement capacity of the wheel， and the tread has sufficient rigidity， which verifies the advantages of designing the coupled flexible tread structure.

In order to improve the rotation accuracy of the machine tool spindle without increasing the mechanical structure complexity， by controlling the active single-sided film throttle valve， the oil film force of the hydrostatic bearing embedded with control recess is actively adjusted to control the radial motion of the spindle. The oil film force of the bearing was obtained according to the Reynolds equation and the flow continuity equation， then， the theoretical model of the spindle motion was established. Theoretical and experimental researches on active control of the spindle radial rotation error were carried out based on PID controller， and the spindle motions under different types of loads were compared and analyzed. The results show that the spindle center can be kept at the target position under fixed load with the control of active hydrostatic system. The amplitude of the spindle motion under dynamic load can be reduced effectively and the orbit center is closer to target position than that without control.

In 2021， CRRC Changchun Railway Vehicles Co.， Ltd. completed the passive safety crash test of the whole electric multiple unit in rail for the first time in the world. However， the crashworthiness optimization method of the front-end energy-absorbing structure of rail vehicles needs to be further studied. Firstly， this paper introduces the equivalent static load method（ESLM） for structural crashworthiness optimization. Secondly， the parametric finite element model of the front-end energy-absorbing structure is created and then verified by experiments. Finally， the ESLM is used to optimize the energy-absorbing structure under the impact condition. The optimization objective is to minimize the mass of the whole structure， the design variables are the plate thickness of the components. The constraint condition is that the collapsed displacement does not violated the allowable value when the absorbed energy of the structure reaches the specified value. The engineering example shows that the ESLM can efficiently solve the crashworthiness optimization problem of the energy-absorbing structure of rail vehicles， which provides a solution for the passive safety design of rail vehicles.

In order to ensure the surface quality and machining stability of turning， the real-time and accurate monitoring of turning tool wear state is realized. A tool wear state prediction model based on wavelet threshold denoising， Long and Short Time Memory（LSTM） network and Particle Swarm Optimization（PSO） was proposed. The improved polynomial threshold function was used to denoise the tool acceleration vibration signal， and the high quality signal input sample was constructed. The wear values of the tool rear face were predicted and the wear states were classified by training the LSTM network. The proposed PSO-LSTM model is superior to the unoptimized LSTM network in terms of prediction and classification accuracy by using PSO.

Based on the central composite design， the three geometric parameters of the air-cooled intercooler shutter fins， the opening angle， the fin spacing， and the louver spacing， are tested with three factors and five levels. Then a complete second-order regression model with the heat transfer characteristics Nu and the flow resistance characteristics f as the objective function is established， and the response surface analysis is carried out to analyze the importance of the influence of each structural parameter， and finally the optimization calculation is carried out according to the multi-objective genetic algorithm. The results show that the heat transfer characteristics are mainly affected by the fin spacing， while the resistance characteristics are most affected by the opening angle. Multi-objective optimization can obtain a set of Pareto solutions， which can be selected by the decision maker according to actual needs.

In the initial design stage of gear system， there is a large fluctuation of transmission error， which leads to prominent vibration and noise problems of the system. Aiming at that， a multi-objective optimization method of macro parameters of gear pair is presented. Based on the potential energy method and slicing method， the analytical calculation formula of transmission error of helical gear is derived. Taking the maximum total contact ratio， minimum fluctuation of transmission error（TE）， and minimum total volume of gear pair as optimization objectives， the gear macro parameters as design variables， and a fast elitist non-dominated sorting genetic algorithm（NSGA-Ⅱ） as optimization algorithm， the macro parameter optimization model of gear system is established. Taking a two-stage reduction gear system as an example， the macro parameters of the gear pair are optimized by using the optimization model， and the dynamic indexes before and after optimization are simulated based on the software MASTA. The results show that under different working conditions， the fluctuation of meshing TE and system TE as well as the vibration displacement amplitude of bearing seat are reduced in different degrees， and the overall dynamic performance of the gear system is improved.

Facing the unsignalized intersection scenario with a high incidence of traffic accidents， a vehicle collision warning method based on V2I communication is proposed to ensure driving safety. First， a method for matching road segments and conflict areas is proposed to screen vehicles that are at risk of collision and the corresponding conflict areas. On this basis， a two-level redundant intersection collision warning method is proposed， including the Dynamic Distance to Collision （DDTC） algorithm， which is used to perform first-level collision warning before the vehicle enters the intersection， as well as the Circular Area to Collision （CATC） algorithm， which is used for secondary collision warning after the vehicle enters the intersection. This method compensates for the limitations of self-vehicle perception through networked information， improves detection efficiency through conflict area matching and safe distance filtering， and improves detection accuracy through dynamic distance difference threshold setting. The test results based on the NHTSA intersection scene set show that compared with the representative Time to Collision method and Time to Distance method， the false negative rate is reduced by 16% and 6%， and the false positive rate is reduced by 88% and 48%， respectively， and the detection time is reduced by about 74%， with higher accuracy and efficiency.

In order to enhance the reliability of cooperative target tracking under unknown localization noise statistics， a Bayesian model is proposed for joint estimation of the states of target vehicle and cooperative vehicle and localization noise statistics parameters. An online variational Bayesian inference algorithm is further developed to realize recursive estimation. The simulation results show that the algorithm can effectively improve the accuracy of target tracking when the statistical characteristics of localization noise is uncertain and changes dynamically. Comparing with non-cooperative algorithm， the tracking error can be reduced by 18.7%~23.6%. In comparison with other cooperative algorithms， the tracking error can be reduced by 4.8%~9.7%.

Aiming at the challenges of trajectory data processing and application brought about by the large error of shared bike riding positioning data and the complexity of bicycle riding behavior， the method of data preprocessing， road network matching and rapid identification of road riding quality is studied. Based on the Hidden Markov Model （HMM）， an improved shared bike riding trajectory data matching method is proposed. Based on the density clustering algorithm， the identification of the parking point set of the trajectory is realized， the characteristics of the parking point set and the regularity of the positioning data are used to realize the calculation of the key parameters in line with the noise characteristics of shared bikes， and the optimal matching algorithm flow between bicycle trajectory data and road network is given. Using the matched trajectory data， a rapid identification method of bicycle traffic riding quality based on outlier test is proposed， and a case analysis is carried out in a certain area of Beijing. The accuracy of the method was verified by this method to be 87.7%.

Vehicle Routing Problem （VRP） with flexible time window is useful to increase the profits of logistics companies by loosening the time constraints with less penalty cost. In this paper， the electric logistics vehicle routing problem with flexible time window （EVRP-FlexTW） is studied as a model driven experimental approach. Firstly， an energy consumption model considering the start-brake process is proposed for electric logistics vehicles to reveal their characteristics of energy consumption. Then the influence of energy consumption is integrated into the EVRP-FlexTW model with minimum total distribution cost in consideration in additional constraints such as vehicle loading capacity， flexible time window and electrical energy. The improved Ant Colony Algorithm is also designed to solve the model. Sensitivity analysis on the trip speed and flexible time window impact to the proposed model shows that reducing the maximum driving speed is generally contributed to get the better routing solution. Satisfying the constraints of flexible time window cannot shorten the distribution distance or reduce the electricity consumption of electric vehicles but can achieve the lowest total distribution cost. As a whole， those founding can come to the proposed model validation.

In order to support the establishment and development of the league for commercial vehicles multi-modal transportation， this paper focuses on the two main issues： league member selection and benefit distribution. First， the organization form of multi-modal transportation league of commercial vehicles is determined by analyzing the existing logistics mode of commercial vehicles in China. Then， the evaluation index system is established considering five factors， which includes transportation capacity， service capacity， information processing capacity， financial capacity and emergency response capacity， on this basis， the method which can be used to select the candidate enterprises of the main members of the league is proposed combined with matter-element evaluation model and entropy method. The labor division model of ant colony suitable for benefit distribution among league members is established by analysis the similarity between ant colony and league members. Finally， a case study is conducted based on the actual operation data of the enterprise. The results of this research show that the model and method proposed can better solve the problems of member selection and benefit distribution， and support the establishment and operation of the commercial vehicles multi-modal league.

Since the trip data based traffic origin-destination （OD） demand needs to be expanded to the whole travelers′ level counts， the sensor deployment strategy and expansion factor inference are studied using mathematical programming theory. The sensor deployment model is presented to determine the optimal quantity and locations of sensors through considering the principle of maximum both link and route ?ow coverage information. Based on the link flows observed from the deployed sensors， the bi-level expansion factor inference model is built. The objective function of upper-level model minimizes the distances between the observed and estimated traffic flows， and the constraints are the relationships between expansion factor， OD Demand and link flow. The stochastic user equilibrium （SUE） is adopted as the lower-level model to derive the OD-link proportions. The sequential identifying sensor location algorithm and iterative algorithm are designed to solve the sensor deployment strategy and expansion factor inference model， respectively. Numerical examples demonstrate that the accuracy of values estimated by integrating sensor deployment strategy and expansion factor inference model can reach to 0.01； the built sensor deployment strategy can also be used to determine the optimal scheme of refitting sensors； and the designed algorithms can make convergence to the equilibrium solutions rapidly. This research has significant promoting effects on developing the theory of mobile phone data based OD demand estimations.

Aiming at the uncertainty and suddenness of emergency material distribution decision-making， a multi-objective robust optimization-based emergency material distribution decision model under uncertain demand is proposed. Based on the analysis of the randomness of emergency demand， the demand uncertainty risk of emergency logistics facilities and the risk on road network interruption， it is determined that maximum sum of satisfaction with the rescue time of the material demand point， minimum the total system cost， and sum of rescue time of the material reaches the demand point as the goal， a multi-objective robust optimization model is constructed and solved by Matlab software. Taking a natural disaster in a mountainous area as an example， the validity and correctness of the multi-objective robust optimization model are verified. The results show that in the distribution of emergency supplies under uncertain demand， decision maker can weigh the pros and cons based on the multi-objective robust optimization model in this paper， and formulate a rescue plan that not only has the ability to deal with risks， but also can respond quickly.

In order to study the route choice behavior of travelers in the mixed traffic of network connected vehicles and non-connected vehicles， according to whether the travelers are connected vehicle users and the obedience degree to the Internet of Vehicles （IOV） information， the travelers are divided into non-connected vehicle travelers， connected vehicle travelers who fully obey the information and connected vehicle travelers who do not fully obey the information. The three types of travelers update their travel routes according to historical experience information， real-time information of the Internet of Vehicles， and the combination of the first two kinds of information. Futher， a hybrid day-to-day evolution model considering the obedience degree of the IOV information is established by using the traffic network theory. From the example， it is found that the network stability under mixed travel flow is better than that under stochastic user equilibrium （SUE）， and the number of days for travelers to reach a stable perceived travel time under mixed travel flow is shorter than that under stochastic system optimal （SSO） equilibrium. The proportion of information travelers and the obedience degree of the IOV information are the main influencing factors of the evolution of network traffic. The model finally evolves to the mixed equilibrium state of SUE and SSO.

In order to investigate the influence of shear span ratio on the initiation and propagation of cracks in reinforced concrete beam， according to Code for Design of Concrete Structures， a group of reinforced concrete beams with shear span ratios of 4.0， 3.0， 2.5 and 2.0 were designed. Through the digital image correlation （Digital image correlation， DIC） technology， the whole process of crack initiation and evolution was captured for the reinforced concrete beam components under the monotonic loading. The distribution and width of the cracks in RC beams were reproduced in different loading levels. The test results are compared with the calculated values by the use of major design codes. The results show that the DIC technology is a realistic test observation method； the distribution of cracks has a very important relationship with the shear span ratio， and the larger cracks of the test beam are mainly distributed in the pure bending section. At the same time， the position and width of the cracks are almost evenly distributed within the pure bending section. This is basically consistent with the crack calculation formula given in major design codes.

Based on the stiffness activation of the compression column， the influence of the axial force area on the critical bearing capacity of double column pier with two-story is studied. Some rules are found and the corresponding calculation method of the horizontal critical bearing capacity of double column bridge with high piers is established， so that the solution of the critical force of column bridge with high piers is greatly simplified. This paper deduces the calculation formula of the horizontal critical force for double column bridge with high piers. These formulas can take into account the influence of the tie beams between the piers， which can make up for the deficiency of the standardized calculation length coefficient method and provide a fast calculation method and formula for bridge engineering design. Finally， three examples are selected for finite element calculation. The calculation results show that this method has good precision and accuracy， which can be suitable for arbitrary node load distribution and used for engineering design and theoretical calculation.

Aiming at the problem of the damage of concrete in salt-rich areas， the self-developed equipment was used to simulate the environment of concrete subjected to sulfate corrosion under dry-wet cycles and alternating load. Ultrasonic nondestructive testing， pore structure analysis， chemical analysis， and stress-strain testing were used to analyze the macro and micro performance degradation laws of concrete damaged by sulfate erosion. The results showed that： The alternating load has a greater acceleration effect on the sulfate attack of low-strength grade concrete. The alternating load increases the content of harmful pores in the concrete， promotes the transmission of sulfate ions， and accelerates the sulfate corrosion reaction of the concrete. Based on the stress-strain curve of sulfate-eroded concrete under dry-wet cycles and alternating loads， with damage as a variable， a fitting equation for the relative peak stress and strain of sulfate-eroded concrete with the damage degree is proposed.

In this paper， the reasonable numerical model of CRCP with double-layer reinforcement under the action of temperature shrinkage is explored and verified. The error analysis and reliability analysis of the finite element calculation results and rectangular coordinate solutions are carried out to determine the reasonable analysis model. The L50 table was used to design the orthogonal test scheme of reinforcement factors， and the sensitivity analysis of design parameters affecting the reinforcement ratio of CRCP with double-layer reinforcement was carried out， and the influence law was studied. Through range analysis， variance analysis and significance evaluation， the sensitivity of each design parameter and the significance of the influence level are judged， the coefficient of linear expansion， temperature drop， reinforcement ratio， bond stiffness coefficient， and elastic modulus of steel rebars have significant effects on the mechanical response of CRCP with double layer reinforcement， and should be used as the main design indexes. It is suggested that the reinforcement ratio ρ according to ρ∈［0.60%，0.80%］、 ρ∈［0.80%，1.00%］、 ρ∈ ［1.00%，1.20%］ three value ranges for trial calculation； Take 1 / 3 of the thickness of CRC slab as the value point of longitudinal reinforcement.

In this paper， the experimental system for high mass flow density flow and heat transfer characteristics with heat back was designed and built with the wound tube heat exchanger as the test piece. The experimental system includes main test cycle circuit， cooling circuit， control system and safety ventilation system. The experimental conditions of the experimental system can cover 0.15~0.85 MPa evaporation pressure， 20~120 kg/（m²·s）mass flow density， 0~1.0 dryness and 0~10 kW/m² heat flow density. The thermodynamic model of the experimental system is constructed， and the temperature， pressure data and equipment load parameters of each node under the experimental conditions are obtained. The energy consumption of the system is analyzed and compared with the existing data. It is found that the energy consumption of the regenerative system with back heat is lower than that of the non regenerative system in each dryness. When the dryness is 0.1， the energy consumption is reduced by 75%， when the dryness is 0.5， the energy consumption is reduced by 43%， and when the dryness is 1， the energy consumption is reduced by 3%.

In order to improve the effectiveness of monocular image depth estimation， a monocular image depth estimation algorithm based on self supervised learning is designed. The absolute depth feature， relative depth feature and position feature of monocular image are obtained， and the logarithm of depth difference is used as the loss function to obtain the point-to-point correlation feature of image； The surface normal loss function is used to deal with the surface normal of the depth map to reduce the surface fluctuation of the image， and the similarity between the image photometric loss function and the two images is calculated； The monocular image is input into the self supervised learning network， the depth amplitude is calculated， and the monocular image depth estimation based on self supervised learning is completed. The experiment is mainly divided into objective evaluation and subjective evaluation. In the objective evaluation part， the accuracy of depth prediction of the proposed estimation algorithm reaches 91.2%， and the edge error of depth map is only 3%. This method has high accuracy. In subjective evaluation， the proposed algorithm is verified to be able to predict the image features and improve the image depth estimation effect effectively.

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.

At present， person re-identification （ReID） still exists some issues to solve， such as background confusion， blocking interference， pose misalignment， and insufficient explanation. In this paper， a new method based on multi-scale feature extraction is proposed for person ReID. Specifically， using the feature learning module to learn attributes and convolving by attribute grouping， which could enhance the interpretability of the model and reduce the impact of interference features by comparing the intermediate property characteristics. Compared with other methods， the new method was tested on three commonly used public datasets. The detailed experiments demonstrate that the new method can efficiently extract semantic information from the middle layer and outperforms other methods tested across datasets.

Selecting fault detectability and isolability set （FDIS） that can detect and isolate all faults is an important step in model-based fault detection and isolation of dynamic systems. This step usually requires the minimal cardinality of FDIS， that is， generating minimal fault detectability and isolability set （MFDIS）. The solution time of MFDIS increases exponentially with the increase of the scale of the problem. BILP complete method is the most common and efficient method to generate MFDIS in dynamic systems， but the efficiency of this method needs to be improved， and the complete solution of large-scale cases can’t be obtained in an acceptable time. In this paper， an approach for generating MFDIS based on partial maximum satisfiability problem （PMS） is proposed for the first time. The concept of MSO equivalent set is proposed， and some MSO sets are represented by MSO equivalent set to reduce the size of the problem. Then， the MFDIS genarating problem is transformed to PMS problem to improve the solving efficiency. The experimental results show that the proposed reduction method reduces the problem size to 8.22% of the original size on average； compared with BILP method， the efficiency of the proposed method is increased by 4.72 times on average.

When the current algorithm is used to filter privacy-sensitive data， only a single approximation acquisition method is used. There are certain limitations in obtaining the approximation， which leads to the problem of high MAE and RMSE values. A privacy-sensitive data filtering algorithm based on fuzzy approximation is proposed. First， the data is reduced in dimensionality through the improved local sensitive hash algorithm E²LSH， and low-dimensional data that is more conducive to the subsequent approximation calculation is obtained， and then the Paillier homomorphic encryption algorithm is used to protect the privacy-sensitive data under the premise of ensuring data security. After extraction， the trapezoidal fuzzy scoring model is finally constructed， and the fuzzy approximation is calculated by the mixed model similarity algorithm of modified cosine similarity and Pearson correlation similarity to complete the filtering of privacy-sensitive data. Analysis of the experimental results shows that the minimum MAE value of the proposed method is lower than 0.82， indicating that the method can effectively reduce the MAE value and RMSE value and improve the data filtering effect.

Aiming at the problems of long mining time， poor mining effect and low mining performance in data mining due to the failure to extract relevant data features before data mining， a fast outlier mining algorithm in uncertain data set based on spectral clustering is proposed. The algorithm calculates the similarity of data according to unequal length sequences， and uses partial least square method to extract the features of uncertain data sets； Then， the data features are calculated based on spectral clustering algorithm to obtain the outlier index of the data； Finally， outlier mining of uncertain data sets is completed by outlier index. The experimental results show that the algorithm has the advantages of short mining time， good mining effect and high mining performance.

Traditional perimeter-security systems either suffer from low positioning accuracy of the target or cannot determine the optimal result generated by multiple groups of sensors in common TDOA. In response to the above issues， this paper designs an intelligent perimeter-security system， by means of seismic sensors and cat-swarm algorithm. First， moving coil seismic sensors are used for collecting the seismic signals generated by targets on shallow surfaces. Then， the collected signals are processed to obtain initial TDOA localization results of the target. Later， these initial results are further optimized via cat-swarm algorithm. Aiming at different application scenarios of the system， this paper designs two communication modes （i.e.， LoRa and Beidou short message） to ensure system communication in remote regions. In order to verify the effectiveness of the optimization algorithm， several experiments are conducted in real world under different parameter settings. According to the experimental results， when the error in propagation velocity of seismic wave is less 10%， the average localization error of the proposed system （i.e.， optimized by cat-swarm algorithm） is less 1 meter. In particular， compared with the initial TDOA localization results， the localization error is reduced by 46.9%.

The prediction height of water-flowing in fractured zone of overburden strata is the difficulty of research. A deep learning model is proposed to estimate the height of the water-conducting fracture zone based on simulation experiments of fiber-optic monitoring overlying rock deformation. The key factors， such as the frequency shift value of the optical fiber sensor， the height of the sensor， the advance length of the working face， the lithology and structure of the overlying rock were select in this model. Firstly， the frequency shift value is decomposed into three components， random frequency shift value， periodic frequency shift value and trend frequency shift value. Then， deep learning model are built to predict each component， and prediction component are superimposed to form the frequency shift value of sensor. Finally， the development height of the water-conducting fracture zone is inferred in accordance with the relationship between curve of frequency shift value change and the development height of fracture zone. The experiments show that the average estimation error of such height is about 11 mm. The predicted heights are accurate and reliable， which can also meet requirements of an engineering test practice.

Existing approaches usually encountered with utilizing single-modal features only， insufficient fusion of multi-modal features， or simple semantic representation learning paradigm. To better solve video memorability prediction， an end-to-end deep multi-modal self-attention network is proposed. The proposed network consists of a semantic representation learning sub-network and a semantic modeling sub-network. The semantic representation learning sub-network uses a generative adversarial learning to better explore the associations between modalities and complete the fusion of multi-modal information； the semantic modeling sub-network uses a self-attention mechanism to refine global information while retaining local information to the greatest extent. Experimental results show that the proposed network achieves better prediction performance in comparison with state-of-the-art methods. In addition， the ablation experiments further verify the effectiveness of the two involved sub -networks in video memorability prediction task.

In order to reduce the secondary pollution during the recovery of residual film in farmland， reduce the cost of storage and transportation of residual film， and realize the standardized management of residual film scrap in farmland， a mechanism for picking up and packing residual film has been designed and developed. The mechanism was mainly composed of transmission system， picking mechanism， scraper type film unloading mechanism， packing mechanism. Through the analysis of the residual film pick-up process and force， the installation angle of the arc nail teeth and the speed range of the pickup roller under the current working conditions were determined； the film scraping process of the scraper type film unloading mechanism was analyzed， and it was clear that the angular velocity range of the scraper film unloading mechanism； the three stages including horizontal conveying， climbing and throwing back formed by the film core of the residual film packing device were studied， the force during the movement was analyzed， it was determined that the main factors contributing to the formation of the film core were the thrust of the subsequent residual film and the frictional guiding force between the steel roller and the residual film. Three factors were taken as test factors： the machine forward speed， pickup roller speed， and steel roller speed. The residual film pick-up rate and the bundle rate were tested by orthogonal experiments. The primary and secondary order of factors affecting the residual film pick-up rate and the bundle rate was obtained as follows： Machine advancement speed>roller speed>baling steel roller speed， under the premise of ensuring the pick-up rate of residual film， the baling rate was maximized. The optimal working parameters of the residual film pick-up baler were determined： The speed of machine advancement was 4 km/h， the speed of the pickup roller was 60 r/min， and the speed of the steel roller was 300 r/min. Taking better working parameters to carry out field verification tests， the residual film pick-up rate and bundle rate were 90.3% and 98.4%， respectively. The residual film pick-up baler ran smoothly and reliably， with low failure rate， and had high use and promotion value.

Aiming at the problems of damage to ears and grains and adulteration of stems and leaves due to the harvesting of the existing fresh corn ear picking operations， imitating the posture of manual harvesting of corn ears， a bionic ear picking device for fresh corn was designed to reduce grain damage on ears， reduce the power consumption， and improve the harvest quality. The structure design and parameter analysis of the ear picking device were carried out. A quadratic orthogonal rotation combination test was used， taking the rotation speed of the picking roller， the angle of inclination clamping mechanism， the gap of the clamping mechanism and the forward speed of the unit as the influencing factors， take grain crushing rate， ear impurity rate， and power consumption as the test indicators. And using Design-Expert software to analyze the variance and response surface of the test data， the mathematical model and factor contribution rate between the influencing factors and the response surface were obtained， and the model is tested and verified. The results show that when the rotation speed of the breaking roller was 465 r/min， the angle of inclination clamping mechanism was 19°， the gap of the clamping mechanism was 8 mm， and the forward speed of the unit was 1.2 m/s， and the corresponding evaluation indexes were as follows： the average grain crushing rate was 0.88%， the average ear impurity rate was 1.3%， the average power consumption was 3.1 kW， and the operation stability of the device is good. Field test results show that the average grain crushing rate was 0.87%， the average ear impurity rate was 1.22%， and the average power consumption is 3.0 kW， which reduces ear impurity and damage， reduces power consumption， and its performance meets the requirements of fresh corn harvesting operations.

Because of the problems of incomplete separation， low efficiency and high energy consumption of the mulching plastic film and cotton stalks after the mechanized operation of cotton fields in Xinjiang in autumn， a separation scheme between plastic film and cotton stalks based on the irrigation diversion canal as the medium was determined. Then the changes in the movement state of plastic film and cotton stalks during the separation process were analyzed and the key components were designed and selected. The four factors， five levels， and five responses were rotated orthogonally which the installation angle of the deflector， the feeding amount of plastic film and cotton stalk， the installation position of plastic film and cotton recovery net and its displacement differences were acted as the test factors， the separation rate of plastic film and cotton stalks， the plastic film content of the cotton stalk recovery net， the cotton stalk content of the plastic film recovery net， plastic film and cotton stalk loss rate and the impurity rate in the recycling net were evaluation indicators. Through data analysis， the fitting regression equation between test factors and evaluation indicators was established， and the analysis of variance explored the influence of each factor and its interaction. The best combination of parameters were obtained by optimization of using the Design Expert. The results showed were that the installation angle of the deflector was 33.75°， the feeding amount of plastic film and cotton stalk was 67.452， the installation position of plastic film and cotton recovery net was 24 cm， and and its displacement differences was 25.76 cm. The relative error was 6.28%， 7.89% and 28.95%， respectively， the film stalk loss rate was 10.00%， the impurity rate in the network was 3.72%， and the performance of the prototype device was reliable. It can provide relevant basis for the optimal design of plastic film and cotton stalks separation methods and device structure parameters.