Based on the existed experimental results， this paper applies the Computational Fluid Dynamics （CFD） to investigate the leading edge structure of the cooling tube and airflow speed on the thermal-hydraulic performance of an air-cooled charge air cooler （ACAC）. Results showed that， under the same fillet radius R₁ the Fanning friction factor f raises with the increasing airflow speed， but at the same airflow speed f decreases with the increasing of R₁， when taking R₁=0.9 mm as a baseline， the f of R₁=0 mm increased at a maximum of 12.42%， the f of R₁=3.6 mm deceased at a maximum of 5.41%； For the Nusselt number Nu， under the same fillet radius R₁ it raises with the increasing airflow speed， but at the same airflow speed it decreases with the increasing of R₁， also taking R₁=0.9 mm as a baseline， the Nu of R₁=0 mm increases at a maximum of 2.22 % and the Nu of R₁=3.6 mm decreases at a maximum of 1.49 %； For PEC， under the same fillet radius R₁ it raises with the increasing airflow speed， and at the same airflow speed， it also increases with the increasing of R₁， again taking R₁=0.9 mm as a baseline， the PEC value of R₁=0 mm decreases at a maximum of 10.13 % and increases at a maximum value of 3.4 % when R₁=3.6 mm. In summary， when R₁=3.6 mm， airflow speed equals 7.5 m/s， the ACAC achieves its best thermal-hydraulic performance， therefore， increasing R₁ and airflow speed is helpful to improve the thermal-hydraulic performance of ACAC.

This paper aims to reduce the workload of gasoline engine bench test and improve the experimental efficiency by numerical simulation technology. The model group prediction method （optimized artificial neural network method） was used to model and predict the steady-state emissions of NOx， CO and HC in the process of gasoline engine bench test. The results showed that the model group prediction method has high reliability and may improve the accuracy of prediction results compared with the traditional single model prediction method. The training data set of neural network modeling may be appropriately reduced by using the method of separating points， and the better prediction ability may still be maintained. In the process of project development， only 30% of the test quantities were needed， and the test results may be used for the training of neural network model， which may better predict the emission of the remaining working conditions. Through the validation analysis of other models， the model group prediction method had a good universality in the prediction process of engine steady-state exhaust.

During the starting process of single clutch， the temperature rise of clutch plate is obvious and the sliding friction power is uneven， which seriously affects the service life of DCT. Based on the configuration of P2.5 plug-in hybrid electric vehicle equipped with DCT， combined with the starting dynamics analysis and considering the driver's starting intention， a starting strategy based on multi-layer fuzzy control is proposed. The driver's starting intention is identified according to the accelerator pedal opening and its change rate， and the clutch combination oil pressure is deduced according to the starting intention to control the transmission torque， at the same time， the clutch separation threshold is judged according to the driver's starting intention. The results show that： compared with single clutch starting， the starting time can be shortened by 12.6% and the impact degree can be reduced by 14.0% while effectively balancing the sliding work of the two clutches.

The study proposes a structural optimization method that takes the material types， cross-sectional shapes and dimensions of beams as discrete design variables for skeleton-type body. A skeleton-type body of a conceptual vehicle was studied. A multi-objective optimization problem was formulated. The total mass， manufacturing cost， the displacements of specified nodes and the maximum axial stresses under multiple load cases were minimized while the first order natural frequency was maximized. An improved non-dominated sorting genetic algorithm， third version， was used to tackle this multi-objective optimization problem. Desirable designs were chosen according to factors of lightweight， cost and performances of the vehicle. The result demonstrates that the proposed method involving multiple types of design variables leads to a better lightweight design as compared to the sizing optimization method.

The study establishes the mathematical model of the EMB system. Based on the PI control algorithm with constant set values in small zones， a hyperbolic secant nonlinear PI control algorithm is devised. It can enhance the adaptability and robustness of the system. Furthermore， the improved variable-structure anti-windup algorithm is adopted to decrease the steady-state error. Static experiments verify the effectiveness of the proposed control algorithm. At last， the equal proportion inertia dynamometer brake experiment demonstrates the feasibility of the electro-mechanical braking system. It provides a theoretical foundation for the application of EMB systems in rail transportation.

The elastohydrodynamic lubrication of ball bearings is closely related to its dynamic performance. The accurate calculation of bearing elastohydrodynamic lubrication is the key to bearing speed analysis and reliability design. In this paper， the quasi-statics and elastohydrodynamic lubrication analysis model considering centrifugal expansion was established， that is， the influence model of centrifugal expansion. The influence of centrifugal expansion effect is analyzed. The results show that the stiffness and damping of the inner contact oil film decrease with the increase of the rotating speed； Considering the centrifugal expansion， the stiffness of the oil film increases and the damping decreases， and the amplitude of the change rate increases with the increase of the rotating speed. When the speed is close to high speed， the calculation accuracy can be improved by considering the centrifugal expansion. However， once the speed enters the high-speed region， the effect of centrifugal expansion can hardly be ignored.

The proposed method builds surface equations of bones， muscle of human limb and robot fingers， divides finite elements of muscle tissue of human limbs， gradually obtains finger force of rehabilitation robot hand， and finally realizes the calculation and analysis of human-robot interaction force. In simulation verification of simplified model， the effects of displacement， position of interaction， elasticity modulus of human soft tissue and Poisson's ratio on human-robot interaction force are revealed， and the average relative error between theoretical calculation and simulation data varies from 10% to 15% when the displacement is 5 mm. The simulation outcomes indicate that the proposed theory is able to effectively calculate finger force of rehabilitation robot hand， calculate and evaluate human-robot interaction force.

In order to obtain the dynamic wear characteristics of elliptic gears， a pair of elliptic gears is taken as the research object. Based on Hertz contact theory and Arcard wear calculation formula， the calculation model of contact stress and wear of elliptic gears is established， and the numerical calculation method is used to simulate the contact stress and wear distribution at the meshing point. The distribution of the contact stress and the wear of the tooth surface with the pressure angle of the tooth profile under different design parameters and working conditions are obtained. The analysis shows that the contact stress of the tooth surface will change suddenly in the alternate region of single and double teeth engagement. With the increase of eccentricity and torque， the contact stress of the tooth surface presents an increasing trend. The amount of tooth surface wear decreases first and then increases from the root to the top of the tooth， and tends to zero in the pitch circle treatment. The amount of tooth surface wear increases with the increase of eccentricity， input torque and rotation speed. Therefore， under the condition of satisfying the given requirements and motion rules， reducing the eccentricity， input torque and rotation speed is conducive to reducing the tooth surface wear. The research results can provide some guidance for the subsequent non-circular gear dynamic wear analysis and gear tooth modification.

Aiming at gear wear in the transmission system of vehicles at low speeds， which affects the service life of the transmission system， a calculation method for tooth surface adhesive wear of involute cylindrical spur gears is proposed based on the load distribution coefficient model considering the depth of tooth surface wear and the Archard wear model. The wear amount of the tooth surface of the spur gear was obtained. The evolution rule of the tooth surface wear was studied， and the accuracy of the model was verified by comparison with the published paper. Meanwhile， the effects of tooth surface load and wear factor on the tooth surface wear depth were studied， and the relationship between the two factors was revealed. The tooth surface load will directly affect the wear coefficient and further affect the tooth surface wear depth. The simulation results show that： the wear amount of area near the pitch point of the involute cylindrical spur gear is almost zero； the wear of the tooth surface near the tooth root of the pinion is the most serious； the amount of wear on the tooth surface near the alternating area of single tooth meshing and double tooth meshing has abrupt changes relative to other positions.

Aiming at the difficulty of deep feature extraction in the shallow learning methods of rotating machinery fault diagnosis and the problem of the small number of available fault samples in practical engineering， a rotating machinery fault diagnosis method based on densely connected convolutional network （DenseNet） and support vector machine （SVM） is proposed， which provides a new idea for the fault diagnosis of key mechanical components of aircraft. First， the continuous wavelet transform（CWT） is used to convert vibration signal segments into time-frequency image samples. Then， the experimental samples are input into the DenseNet model for feature extraction. Finally， the extracted features are used for the training of SVM， so as to realize the fault diagnosis of rotating machinery. The simulation results show that the proposed method has higher diagnostic performance than other advanced models， which proves the effectiveness and feasibility of the method.

To precisely evaluate key working performance such as driving power， the stress state of the screw with liners， and product particle size distribution of large vertical screw stirring mill， a fluid-structure interaction method （CFD-DEM coupling） was conducted for the calculation. To be more specific， the particle motion distribution， interactive force distribution， collision rate， and power consumption were evaluated under different working conditions. Furthermore， the load applied on the screw with liner was extracted from coupled simulations into FEM calculations for the stress state analysis based on which the value and distribution of maximum stress were studied. Besides， a numerical model was constructed for the prediction of product particle size distribution of vertical screw stirring mill to accomplish the evaluations of the impact of different milling parameters on the results. Field tests were taken for the validation of the effectiveness of coupling simulations based on the data of driving power under different amounts of milling balls. Lab experiments were conducted for the determination of the parameters applied in the theoretical model. According to the fitting results of the lab data， a numerical calculation program was established for the prediction of product particle size distribution under different milling conditions. The analysis results indicate that the rotate speed of screw with liner， the filling rate， and the size of milling ball are the main factors that have complex influence on the working performance of a vertical screw stirring mill.

To enhance the safety and efficiency of traffic flow operations at an intersection using left-turning guiding markings， the lane selection behaviors and vehicle trajectory characteristics of left-turning vehicles from an approach to an exit are analyzed. According to the random utility theory， the exit lane selection models for left-turning vehicles are formulated. Using the circular and horizontal curves， the vehicle trajectory models for left-turning vehicles are described. Two kinds of left-turning guiding markings and three types of guiding modes for left-turning traffic flow are proposed. Traffic flow simulation models are constructed， six design scenarios are compared， and the impacts of the percentages of vehicle types on the guiding effects are analyzed. The outcomes reveal that： the approach lane， self-type， turning angle and the type of the preceding vehicle in the same lane for the target vehicle significantly impact on the exit lane selection behaviors of left-turning vehicles； the circular and horizontal curves are both suitable for regular and irregular intersections； the settings of the horizontal curves for guiding left-turning vehicles can not only standardize the order of traffic flow operations but also improve the performances of traffic flow operations； it is not suitable to install redundant guiding markings in order to balance traffic efficiency and the number of stops； these conclusions are not impacted by the percentages of vehicle types. At intersections on urban roads， it is suggested that the horizontal curves should be used to install the guiding markings for left-turning traffic flow. When the number of left-turning lanes is more than one， it is recommended that two left-turning guiding markings should be installed at least.

In order to develop a more efficient train operation adjustment scheme in terms of recovering train delay and easing of passenger detention， an integrated train operation adjustment method was put forward， which includes a time-exceeding strategy， a train deduction strategy， a skip-stop strategy and a dynamic dwell time. Then， combined with the actual operating conditions of the train， the operation constraints and adjustment constraints were set up， and a model with the target of minimizing the total travel time was established. Finally， according to the characteristics of the model， a corresponding nested genetic algorithm was designed to solve the model. The results of the case show that compared with the conventional train operation adjustment method， the proposed integrated adjustment method can make the train which was directly affected by delay complete the delay recovery in advance in various kinds of situations， and can effectively alleviate the long waiting time or detention of passengers caused by the delay.

This paper aims to analyze the travel characteristics of highway on holidays and establish the travel behavior decision-making model. First of all， a total of 500 valid questionnaires were collected from the survey and analysis of highway travel behavior characteristics on holidays. Secondly， considering the difference of individual risk preference， a freeway travel time selection model based on nested logit -cumulative prospect theory model is proposed to further understand highway travel behavior decision-making in holidays. Finally， a case study is carried out on the holiday Expressway in Guangdong Province， and the accuracy of the model is tested. The results show that the overall prediction error of the proposed model is 14.14%， and prediction error of the nested logit model is about 22.22%. Compared with other models， the proposed model can improve the accuracy of prediction results.

In order to analyze the influence of charging queuing time on the path selection of electric vehicle users and network equilibrium， a mixed traffic network model was established by considering the charging queuing time in relation to charging flows and charging time in relation to the charging amount. We proved the uniqueness of the solution and derived the KKT conditions corresponding to the mixed traffic network equilibrium， which was proven to be equivalent to the Wardrop's first principle. The solution algorithm was designed based on the gradient projection method and an example network was tested to verify the effectiveness of the model and the algorithm. The research results show that the charging flow distribution among charging stations corresponding to the optimal solution under the fixed queuing time is not unique， while it is unique under the non-fixed queuing time. With the non-fixed queuing time， charging flows distribute more evenly among charging stations as travelers try to avoid excessive charging queuing time； Service capacity of charging stations has a significant impact on the balance of the utilization rates of charging stations； Total charging queuing time and total trip time gradually decrease as the service capacity of charging stations increases.

Under the influence of technology， cost， environment and other adverse factors， data missing and data noise problems inevitably exist in the data transmission process of the vehicle networking platform. In view of the practical problems that need to be further improved in the data transmission optimization of the Internet of vehicles， based on the vehicle networking platform of a commercial bus enterprise， this paper obtains the driving cycle data of the bus running on the fixed line. According to the characteristics and problems of the data of the vehicle networking platform， the missing data of driving cycle is estimated by the integration method of interpolation and neural network， and the noise data of driving cycle is cleaned based on wavelet transform. Finally， the evaluation index of the data processing method of driving cycle is given， and the effect of the data processing method is explained reasonably. The results show that the missing data track method can better estimate and restore the change of the real working condition of the vehicle. The method based on wavelet transform effectively processes the noise data， and the errors of both methods are within a reasonable range. The research content of this paper can provide practical reference and reference for the optimization of data transmission and the processing methods of massive and high concurrent data in the field of Internet of vehicles.

The self-developed Nicigo driving-recorder was used to collect the driving data of 50 heavy semi-trailers carrying hazardous cargos in Ningbo city according to the 24-hour free running route method. 3006 kinematic fragments were obtained through data segmentation technology. Principal component analysis and K-means clustering technology were used to classify the kinematic segments. Various representative segments were selected according to the distance between the kinematic segments and the clustering center， and the driving cycle of heavy semi-trailers carrying hazardous cargos in Ningbo city were finally fitted. By comparing with the representative driving cycles at home and abroad， it is found that the average speed， acceleration and deceleration ratio of the constructed cycle are very close to the FTP-75 cycle， and the uniform speed ratio is the least different from that of heavy trucks in Beijing， and the idle speed ratio is greatly different from that of various cycles.

To reduce the impact of the missing traffic data on traffic flow prediction， advanced driving assistance， traffic state estimation， and other traffic applications， an adaptive rank Tucker decomposition-based imputation approach （ARTDI） is proposed to recover the missing data and improve the data quality. Multi-lane traffic flow data are represented as a tensor to make full use of the spatial-temporal characteristics of traffic flow. The objective function of the imputation approach is determined through Tucker decomposition and solved by the momentum gradient descent method. Three missing types of traffic datasets including the completely random missing， random missing， and mixed missing were constructed based on the lane-scale traffic speed data of the multiple road sections of the expressway in Beijing. The experimental results indicate that the mean absolute percentage errors （MAPE） of the ARTDI approach are 11.67%， 12.03%， and 11.89% respectively under the three missing types. Besides， the results demonstrate that with the increase of the missing rate， the ARTDI approach outperforms the benchmark approaches in terms of recovery accuracy under different missing types and the MAPEs of the ARTDI approach does not increase significantly. Hence， the ARTDI approach is stable and applicable.

The elasto-plastic stress-strain behaviors with double yield surfaces of bioenzyme-treated expansive soil was studied based on Lade model. Firstly， a series of the isotropic consolidation drainage tests and the triaxial consolidated drained shear tests of bioenzyme-treated expansive soil under different ratio of bioenzyme-based soil stabilizer were conducted. Secondly， the expressions between the parameters of the Lade model and the ratio of bioenzyme-based soil stabilizer were fitted based on the triaxial test results. Finally， a Lade model based on the ratio of bioenzyme-based soil stabilizer was proposed. The study conclusions as follows： ①The bulk strain{\epsilon }_{\mathrm{v}} increases nonlinear with the average stressp， and decreases with the ratio of bioenzyme-based soil stabilizer at p is constant. The test curves of {\epsilon }_{\mathrm{v}}-pare nonlinear relationship. ②The axial strain{\epsilon }_{\mathrm{1}}， lateral strain {\epsilon }_{\mathrm{3}} increases nonlinear with the deviator stress q respectively， and q increases with the ratio of bioenzyme-based soil stabilizer. The bulk strain{\epsilon }_{\mathrm{v}} decreases with the ratio of bioenzyme-based soil stabilizer. The test curves of q-{\epsilon }_{\mathrm{1}}、{\epsilon }_{\mathrm{1}}-{\epsilon }_{\mathrm{v}}、q-{\epsilon }_{\mathrm{3}} are hyperbola respectively. The stress-strain relationships between q and {\epsilon }_{\mathrm{1}} are strain hardening， and the relationships between q and {\epsilon }_{\mathrm{v}} are shrinkage. ③A Lade model based on the ratio of bioenzyme-based soil stabilizer can better describe the effects of the ratio of bioenzyme-based soil stabilizer on the elasto-plastic stress-strain behavior of bioenzyme-treated expansive soil， and can well predict the stress-strain relationship of bioenzyme-treated expansive soil under different ratio of bioenzyme-based soil stabilizer and confining pressures{\sigma }_{\mathrm{3}}.

Joint between concrete filled special-shaped steel tube column and steel beams usually adopts inner diaphragm or outer diaphragm. However， the outer diaphragm will hamper the function of the architecture， and it is inconvenient to process the inner diaphragm， which will also obstruct the pouring quality of concrete. Therefore， a new joint combined by local thickening the tube walls in the joint zone and using a Z-shaped cantilever-beam splicing segment is presented in this paper. The concrete modifications are： （1） the outer diaphragms were replaced by local thickening the tube walls in the joint zone， （2） the Z-shaped cantilever-beam is employed to connect the concrete filled special-shaped steel tube column and steel beam. According to the strong column weak beam principle， 6 one half scaled specimens of the joint between concrete filled special-shaped steel tube column and steel beam were designed and manufactured， the number of the new joint and the joint with outer diaphragm are all 3， the column sections are L shape， T shape and 十 shape， respectively. The low-reversed loading tests for the aforesaid 6 specimens were carried out， their failure modes and the seismic behavior， such as hysteresis curve， skeleton curve， strength degradation curves， stiffness degradation curve， ductility coefficient， total energy dissipation and equivalent viscous damping ratio， were obtained. The results showed that the new joints had the relatively high bearing capacity， better ductility and energy dissipation capacity in comparison with the joint with outer diaphragm. The energy dissipation of the new joints included the contribution of the material yielding and the contribution of the slipping between the plates in the splicing area of the Z-shaped cantilever-beam and the steel beam， and the ductility of the joint was significantly improved by the slipping.

In order to avoid high hydration heat of precast concrete T beam during construction and maintenance， early vertical cracks appear at the beam end. Taking a 40-meter prestressed concrete T-beam as the research object， the mechanical properties of the concrete t-beam during the pouring process were monitored and tested. According to the measured results， a refined finite element model considering the early time-varying of the beam segment was established. And based on the above model， the evolution characteristics of hydration heat temperature field and the stress distribution law at the end of the beam during pouring process were studied. The results show that in the initial stage of concrete pouring of the 40-meter-span precast T-beam， the position near the beam end has a higher risk of cracking due to the rapid accumulation of temperature during the hydration process. In the area 2m～4m away from the beam end， the cracks are easy to penetrate through the web near the flange plate. It is recommended to control the mold-in temperature within 20 ℃， and adopt the method of optimizing the mixing ratio， reduce the amount of cement or use low-heat-of-hydration cement to reduce the heat of hydration and prevent cracking.

To investigate the mechanism of mud pumping of road base on silty clay foundation， a self-made apparatus has been designed and manufactured. The subbase and subgrade were simulated by graded ballast and silty clay， respectively. A series of mud pumping tests had been conducted to investigate the development law and occurrence condition of mud pumping with different fines content and void ratio. The experiment results show that the pore water pressure accumulates under the cyclic loading. When the effective stress turns negative， the soil liquefaction occurs， which leads to the mud pumping of the road base. Furthermore， the fine content and void ratio are important parameters to mud pumping in silt clay foundation， the development of mud pumping can be aggravated by the increase of the fine content and the void ratio. A criterion including plastic index （I_P ） and void ratio （e） of the foundation has been proposed to determine the occurrence of mud pumping. When above the criterion， the foundation is susceptible to mud pumping. The study can provide a reference for prediction and control of the mud pumping of road on soft soil.

In order to meet the requirements for use inside the main cables of suspension bridges， a dry air supply conduit structure with good compressive and extension properties is designed， and the compressive properties and air supply resistance characteristics of the air supply conduit are studied by numerical simulation and experimental testing. The results show that the composite air supply conduit composed of the 304L stainless steel spring with an inner diameter of 50 mm， wire diameter of 5 mm， pitch of 15~20 mm and the corrugated pipe can withstand the radial compressive stress of 13.4 MPa， thus meeting the compressive strength requirements. The air delivery resistance coefficient of the composite conduit composed of the spring and the corrugated pipe is significantly smaller than that of the pure spring conduit， with a maximum reduction of 46.4%， which means that the composite air supply conduit can effectively reduce the pressure loss in the process of dry air delivery.

In order to improve the buffeting response calculation method for cables of long-span cable-stayed bridge， the shortcomings of bridge buffeting response spectrum method are analyzed. Based on bridge buffeting response spectrum method， the aerodynamic damping calculating formula suits for cables in long-span cable-stayed bridge is given， and the approximate formula for the cable buffeting response root mean square along-wind in long-span cable-stayed bridge is proposed. The applicability of the approximate formula is analyzed. The results show that the approximate formula has a good accuracy when the wind speed is higher than 40m/s， meanwhile the sag parameter is between 0.76 and 2.29. The root mean square of cable buffeting response along-wind can be calculated conveniently by this formula， which can provide buffeting response study and engineering analysis with an effective method.

A real-time traffic status data acquisition method based on real-time traffic map was first proposed， and then a method that can convert real-time traffic status into real-time traffic volume was put forward. Based on this real-time traffic volume data and the classic delay calculation method based on queuing theory， the delays caused by maintenance operations at different start time can be calculated， the total cost due to the maintenance can be further obtained and compared， and therefore the optimal maintenance operation start time can be obtained. The reliability of the real-time traffic status data to real-time traffic volume data conversion method and the feasibility of optimizing the maintenance operation start time based on real-time traffic map data are verified by actual cases.

Aiming at taking both color information and edge retention into count as much as possible， an effective multi-scale fusion method based on structure extraction and visual salience map （VSM） is proposed. Firstly， the source images are decomposed by a validated structure extraction from the texture via relative total variation. Then， preserving the color of MS and the edge definition are considered in the fusion of base layers and the conventional 'averaging' fusion scheme is unable to achieve the same effects. Next， VSM-based technique is proposed to fuse the detail layers to figure out perceptually and combine salient visual structures， standing out from neighbors. Finally， the desired fusion image was obtained by image reconstruction， using the detailed and based fused results. Remote sensing images from the Worldview-2 satellite are used as the data set which verifies the effectiveness of our algorithm. Experiments show that the algorithm designed in this paper retains the detail information of the panchromatic image and the spectral information of multispectral image， and highlight the expediency and suitability with comparing with others.

In the task of few-shot image classification， the extremely limited number of labeled examples per class can hardly represent the real class distribution effectively， which is the main reason for misclassification. To tackle this problem， we propose a method which named Sliding Feature Vectors Neural Network （SFV）. The method aims to assemble all the local sliding feature vectors of samples from the same class to construct the class-level feature spaces， and then it utilized the image-to-class measure to classify the query samples. That means on the measure stage， SFV compare the similarity between the class and the query sample. SFV expands the class feature space by adding the edge information of feature blocks and correlation of their position and structures to maximize the utilization of the deep feature maps when the sample is extremely limited， which can ease overfitting problem caused by small sample data. Experimental study on benchmark datasets consistently shows its superiority over the related other framework， especially on fine-grained datasets， it achieves state-of-the-art.

in order to access the linked validity more efficiently and accurately. This paper studies the methods and techniques of link validity assessment at home and abroad， and finds that some of the results of link validity research are only briefly mentioned in some literatures. Therefore，in this paper， an algorithm?for URI validity assessment is proposed， and the validity and efficiency of the algorithm are proved by theoretical analysis.Finally， the open data published by DBpedia was used for experimental verification. Through comparison of experimental results， the assessment effectiveness of this method was improved by 0.1% and the evaluation efficiency was four times higher than that of the conventional method.

Reference evapotranspiration （ET₀） is a key factor affecting modern hydrological research， and is one of the core variables to be controlled in the process of crop transpiration. An improved genetic whale optimization algorithm （GWOA） and ET₀model of BP neural network were established. The traditional whale optimization algorithm was replaced by the selection mechanism and adaptive mutation factor in genetic algorithm， The results show that the optimal search agent selection of WOA optimizes the population diversity and the ability to jump out of the local optimum of the whale algorithm， likewise significantly improves the fitting accuracy and convergence speed of the whale optimization algorithm by changing the convergence factor and weight factor of the whale algorithm. In the ET₀ simulation of five stations in Northern Shaanxi Province， the basic meteorological data were input as elements， The calculation results of FAO P-M model were used as reference value， and the simulation results of GWOA-BP model were compared with those of other algorithm models. When there is only temperature data， GWOA-BP model can still reflect the nonlinear constraint relationship between meteorological factors and ET₀ （average R² is 0.990， average R_MSE is 0.287 mm/d）， which can completely replace the traditional model hargreavers model （R² increased by 5% and R_MSE decreased by 63%）；The simulation data under different meteorological factors input show that the important meteorological factors of ET₀ in Northern Shaanxi Province are T_max、T_min、n、RH and u₂； the simulation data under the same meteorological factor input show that the fitting accuracy of GWOA-BP is higher than that of WOA-BP and BP models.

K-means algorithm is easy to fall into local optimal solution， which leads to poor optimization effect of objective function， especially when k-means is applied to data sets which the number of data elements， data dimension and clusters are large. To solve this problem， iterative k-means minus–plus（I-k-means-+） is proposed in 2018， as a new clustering algorithm of objective function optimization for k-means， which improves iteratively the quality of solution of clustering by removing one cluster （minus）， dividing another one （plus）， from the generated clustering solution， and updating each cluster with topical k-means. However， I-k-means-+ algorithm roughly estimates the gain value and cost value of each cluster， sacrifices precision for lower computational complexity and affects the optimization of objective function. Focus on this problem， in this paper， there is a modified I-k-means-+ algorithm called SP-k-means-+ which propose to calculate the gain value and cost value of each cluster more accurately， based on simulated partition of every cluster. It reduces the possibility of missing detection and misjudgment in the process of cluster pair matching and avoids the redundant computation caused by invalid iteration in I-k-means-+ algorithm， selects more suitable cluster pairs to divide or delete in each iteration. SP-k-means-+ algorithm optimizes the objective function further， improves partial computing efficiency， and the simulated partition is accelerating by a new heuristic acceleration algorithm. Results of experiments according to real data set show that， given the same initial clustering solution， when there is no -+ process， the efficiency of SP-k-means-+ algorithm is improved by 16%； when there is -+ process， SP-k-means-+ algorithm is improved by 10%—47% in objective function optimization compared with I-k-means-+ algorithm， meanwhile two algorithm has close runtimes.

As a hot topic in natural language processing， overlapping region recognition needs to be further explored and studied. Aiming at the problem of poor accuracy and recall in traditional text overlapping region retrieval methods， a large-scale semantic text overlapping region retrieval method based on deep learning is proposed. Combined with sparse automatic encoder and depth confidence network， a hybrid model is constructed. According to the hybrid model， a text classifier is designed and constructed. The main components of the classifier are text preprocessing， feature learning and classification retrieval. In this paper， a series of preprocessing， such as de-noising， word segmentation and stop word removal， are carried out. Finally， softmax regression is used to realize text classification， and the learned text features are used as the input of the classifier to get the classification and retrieval results of the overlapping regions. The experimental results show that the accuracy and recall of the method are both high， showing reliability and robustness.

At present， most anomaly detection algorithms only train models through normal samples， but the lack of abnormal sample may cause misjudgment. This paper proposes an anomaly detection algorithm based on effective anomaly sample construction. The clustering clusters representing different types of normal events are obtained by K-means clustering algorithm. Then， the abnormal samples are constructed based on the temporal relationship of abnormal events. Combined with the abnormal samples constructed in this paper， the classifier is trained by binary classification support vector machine algorithm to transform the detection task into classification task， thereby improve the detection accuracy. The effectiveness of the algorithm is verified on the classic dataset（the Avenue dataset）， and it is found that the detection accuracy of this algorithm is better than some advanced algorithms. Therefore， making full use of the timing relationship of video to construct abnormal samples can effectively improve the effectiveness of abnormal detection.

Focusing on the problem of the low system efficiency caused by multi-tags collision of RFID system， this paper proposes an anti-collision algorithm based on query tree， intended to reduce system energy consumption and improve communication efficiency. The method divides the tags into two groups and segments the IDs so that the tags respectively respond to the prefix and suffix in the query command and only transmit the partial ID sequence of the query segment according to the state code. Compared with the traditional query tree algorithm， the algorithm significantly reduces the transmission of bits useless for identification， eliminates idle time slots， and improves system throughput. Theoretical analysis and simulation results show that the proposed algorithm performs better than the existing query tree anti-collision algorithm and helps to design an efficient RFID identification system.

To prevent manipulation of image content （such as splicing）， this paper proposes an image manipulation localization algorithm based on Channel Attention Convolutional Neural Network， and it is called CA-Net. Although the powerful feature learning and mapping capabilities of CNNs can sequentially acquire rich spatial features， this paper proposes to use parallel dilated convolutional layers with different sampling steps to extract multi-scale features. At the same time， in order to make better use of the characteristic channel information， we additionally introduce a channel attention module in the decoding network. This experiment uses Synthesized image dataset for training， and fine-tunes and tests NC2016 and CASIA on the two image libraries. Experimental results show that the proposed parallel dilated convolutional layer and channel attention module can significantly improve the results. Compared with some of the state-of-the-art algorithms， CA-Net performs best on the two standard image datasets.

In order to pursue higher recommendation quality and faster recommendation efficiency， a personalized recommendation algorithm based on Mir and k-means tag clustering is constructed with music resources as the research objective. The pitch change and contour are selected as the system processing data， and the input， preprocessing， feature extraction， similarity matching and output modules are used to construct the music information retrieval system. According to the selection relationship between users and resources， a multi-mode network is formed. The dynamic multi-dimensional network model of the retrieval system is established through the extension， local range definition and connection priority stages Tag clustering searches neighborhood users to obtain the nearest neighbor user set. The comprehensive eigenvalue is set as the initial clustering center， and personalized recommendation is realized according to the sorted recommendation resource prediction results. The performance of the algorithm is verified by the average absolute error， accuracy rate and recall rate. After the comparison of experimental data， it is found that the accuracy of the proposed algorithm is ideal， the error is small， and the recommendation effectiveness is superior.

Traditional teaching quality evaluation methods require a large amount of human and material resources to be uniformly allocated and the input data are not stored in a uniform way. Such methods suffer from low evaluation efficiency and low evaluation accuracy. Therefore， this paper proposes a design method of teaching quality fuzzy comprehensive evaluation system based on K-means clustering algorithm， which constitutes the overall structure of teaching quality fuzzy comprehensive evaluation system through data preparation module， teaching quality fuzzy evaluation module， report processing module and system maintenance module. The hierarchical analysis method is used to calculate the weights corresponding to the talent quality evaluation indexes， construct the teaching quality fuzzy evaluation model， solve the teaching quality fuzzy comprehensive evaluation model by K-means clustering algorithm， realize the evaluation of teaching quality， and complete the design of the teaching quality fuzzy comprehensive evaluation system. The experimental results show that the proposed method has high evaluation efficiency and high evaluation accuracy.

In order to solve the problems of energy saving management， such as poor measurement accuracy of internal energy consumption， inconsistency of scientific energy consumption index and evaluation standard， and insufficient level of energy consumption management， the MEA-BP neural network predicting the building energy consumption is established. Based on the historical energy consumption data output from the energy consumption supervision platform， the BP neural network and MEA-BP neural network were trained. By comparing the actual value with predicted value on energy consumption， it is found that MEA-BP model is more accurate in predicting energy consumption. The MEA-BP model combined with energy consumption supervision platform could improve the energy saving efficiency about water， electricity， heating and gas， achieving the optimal energy consumption control and efficient use of energy.

In order to improve the defect of relying too much on manual statistical analysis of students' examination results and unable to mine the useful information contained in the results， a statistical analysis system of students' examination results based on decision tree classification technology is designed. The data layer of the system uses SQL Server 2000 database to store all the data during the operation of the system； The application layer of business logic collects the data of students' test scores from the data layer and processes the relevant business function logic. The C4.5 Classification Technology of decision tree is used to realize the accurate classification of massive students' test scores. The C4.5 Classification Technology of decision tree selects the classification attributes based on the information gain rate and uses the post pruning method to process the data to improve the data classification accuracy； The statistical analysis results of the business logic layer are transmitted to the users through the browser application layer using the web server. The system test results show that the designed system can statistically analyze the examination results of different professionals according to the user needs， and still has high statistical analysis accuracy in the case of noise.

In order to solve the problem that motion control performance is easily influenced by measuring noise of the feedback signal， a measuring noise rejection method of motion control system is proposed based on a fractional Bode’s ideal cut-off filter （BICO）. Characteristics of the fractional BICO filter are analyzed， and a linear motor trajectory tracking motion control system is constructed based on an extended state observer. Compared with the conventional low-pass filter and the control strategy without a filter， the trajectory tracking errors of the motion control system without payloads， with system parameter variations， and external disturbances are given respectively. Comparative simulation and experimental results illustrate that the fractional BICO filter can deal with the measuring noise effectively， and therefore the motion control accuracy is improved.

Aiming at the path planning and tracking control problem for narrow parallel parking space， a novel parallel parking path planning method with high calculation efficiency and wide application range is proposed by combining numerical optimization method and arc-line-clothoid curve combination method， and a novel parallel parking path tracking sliding mode control strategy is proposed based on non-time reference path tracking error model. Firstly， the parallel parking path is divided into two parts： the entry path， which is used to guide the vehicle to approach the parking space as close as possible， and the adjustment path， which is used to guide the vehicle to adjust its target pose. The entry path is obtained by solving the constraint optimization problem established by arc-line-clothoid curve combination method， and the adjustment path is planned based on arc-line combination method. Then， the non-time reference path tracking error model is established to simplify the three-dimensional path tracking control problem to two-dimensional problem， and a parallel parking path tracking sliding mode control strategy with few calibration parameters and strong robustness is designed based on the non-time reference path tracking error model. Finally， the simulation is carried out to validate the effectiveness and feasibility of the proposed method， and the simulation results show that the proposed method can guide the vehicle to complete the parking operation quickly， stably and accurately in the case of narrow parallel parking space and the non-zero initial parking azimuth.

Combined with the present status of silage demand in China and the problems of low mechanization level， poor adaptability and versatility， complicated operation process， high labor intensity as well as high cost in oat gathering， giving full consideration to the application of the silage oat collecting equipment and the status quo of its development， the 4QJ-3 silage oat collecting and cutting header was developed. At the same time， the core components such as the picking and conveying mechanism， the forced feeding mechanism and the structure of chopping and kneading were designed， and the field performance test was carried out. With the combination of the throwing blade of spiral protrusion and kneading board， the material can be kneaded and thrown effectively. The results of field trials showed that the prototype and its components work well. When the tractor's forward speed is 2.7~6.8 km/h， the missing piking rate of the prototype is 0.6%~1.1%， the material cutting length is 20~36 mm， the kneading rate is more than 93%， and the pure working hour productivity is 0.60~1.49 hm²/h. Though all the performance indexes are in good agreement with the design values， but there are still many problems in the prototype when the forward speed is high and the continuous operation time is too long. In the later stage， it is necessary to optimize and improve the machine and key parts， and conduct field test to improve the operation efficiency and reliability of the machine. The development of the prototype can lay a theoretical foundation for the research and development of other types of silage harvester.

Existing rape combine harvester， which can complete multiple working procedures at one time and takes the advantage of efficient and time-saving， has the challenges that the cyclone cleaning system is significantly different when the mass ratio of the components under sieve of the rape combine harvester is different. Based on the self-developed 4LYZ-2.0 rape combine harvester， a two factor experiment was carried out， in which the rotation speed of the chopping drum and threshing drum of the combined longitudinal axial flow device of cutting and throwing was taken as the factors. The mass ratio of the components under sieve is regarded as the experimental factor of the quadratic rotation orthogonal combination test， and constructs the regression model between the mass ratio of the pod shell and the short stem， the cleaning rate and the loss rate. Based on the multi-objective optimization， the better change range of the mass ratio of pod shell and short stem was defined. The results of two factor test showed that when the rotation speed of chopping drum was 430~550 r/min， and the rotation speed of threshing drum was 450~650 r/min， the change range of pod shell， short stem， light impurity， and the proportion of rapeseed were 20.86%~31.68%， 9.12%~16.47%， 8.36%~11.27%， 50.05%~51.93%. The multi-objective optimization results showed that when the proportion of pod shell was 20.00%~27.67% and the proportion of short stem was 9.00%~12.08%， the loss rate of cleaning system was lower than 6% and the cleaning rate was higher than 94%. The field test shows that when the threshing clearance is 30mm， the rotation speed of the cutting drum is 550 r/min and the rotation speed of the threshing drum is 450 r/min， the entrainment loss rate of the threshing device is 2.01%， the loss rate of the cyclone cleaning system is 5.39%， and the cleaning rate is 94.62%. 4LYZ-2.0 rape combine harvester can meet the working requirements. By adjusting the operating parameters of the vertical axial flow threshing and separating device， the proportion of the components under sieve can be adjusted to achieve better cleaning performance.

In response to frequent rollover safety accidents on the transverse slope of the zero turning radius lawnmower in recent years， based on the quasi-static dynamics theory， a parametric mechanical model that can predict and analyze the instability of the ZTR lawn mower under ramp conditions is established. According to the actual working state of the mower， the driver factor and the influence of different specifications of cutting tools on the vehicle rollover stability under the suspension and traction state are added. The theoretical rollover prediction equation was solved using Matlab， and the relationship between the lateral load transfer rate of the lawnmower （M-LTR） and its rollover sensitive parameters was analyzed under different working conditions. Using ADAMS to conduct multiple sets of simulation experiments on the lawnmower， further verify the accuracy of the parameterized model. Through experiments， it is found that the traction cutter at the bottom of the lawn mower has stronger anti-rollover performance than the suspension cutter： the critical roll angle of the 1.2m and 1.8m suspension cutters is increased compared with the bare car about 7% and 10%； with 1.2m and 1.8m and specifications of the vehicle with traction cutting the critical roll angle are improved compared with naked car about 9% and 21%. According to this characteristic， it is recommended that when designing working instruments for agricultural or garden vehicles， priority is given to the bottom center traction structure to improve the safety of various vehicles working on slopes.

By UV-sunlight and ultrasonic compound mutagenesis method was used to select beer yeast, and its influence on beer brewing was studied. The research shows that the optimum strain Y7 obtained by UV-sunlight and ultrasonic waves for compound mutagenesis and screening of beer yeast strains with low-diacetyl production were genetically stable after 10 generations. Compared with the original microbe， there was no significant difference in the content of diacetyl and other substances in beer brewed by this strain （P>0.05）.And the difference was significant （P<0.0001），and it can reduce the diacetyl account by 33.04%.And the alcohol content can be improved by 2.2%， and the fermentation degree was improved by 3.6%.The research on the difference of brewing technology and fermentation cycle of the mutant strain， shows that there were significant differences in fermentation index and fermentation cycle （P<0.0001），and the strain can greatly improve the beer fermentation capacity and the ability of anti-miscellaneous bacteria. The application of mutant strain Y7 in beer enterprises can greatly improve beer fermentation ability and resistance to miscellaneous bacteria， and improve beer flavor quality. And it can also improve the quality of beer flavor and shorten the main fermentation cycle by 1/3.

Based on the phase change expansion of the freezing process， the study proposed an anti-icing model. A gradient of the phase change temperature was established at the interface between ice and substrate to interfere with the stability of ice adhesion. Pits were processed on the surface of Aluminum alloy and Polymethyl methacrylate（PMMA） for filling solutions with different freezing points. Ant the ice adhesion strength on the substrate with the phase change gradient and the normal substrate were tested， respectively. The results showed that whether the substrate was Aluminum alloy or PMMA， the sample with the phase change gradient could cause the ice to peel off actively. The change curves of the swelling forces of the aqueous solutions in the pit during the freezing process were measured， and the curves were fitted with function. Combined with finite element simulation， the influence of the phase transition temperature gradient on the substrate surface on the ice adhesion interface stability was analyzed. The analysis showed that substrate with the phase transition temperature gradient in the low-temperature environment made the ice adhesion interface form stress concentration， which destroyed the ice adhesion interface stability. Additionally， the swelling force generated by the aqueous solution in the adjacent pits changed the stress and strain of the non-pit area of the ice adhesion interface， and the stability of the ice adhesion interface in the non-pit area was disturbed. And this would further affect the ice adhesion stability on the material surface.