Abstract:To investigate the issue of snow accumulation and icing in the region of the permanent magnet direct-drive （PMD） bogie of an intercity EMU （electric multiple unit）， this study employs a full-scale three-car model of a specific intercity EMU. Using the Realizable k-ε turbulence model with the unsteady Reynolds-averaged Navier-Stokes （URANS） method and the discrete phase model （DPM）， the snow and wind movement characteristics in the PMD bogie region and the conventional bogie region were simulated under conditions of -30 ℃ ambient temperature and an operating speed of 160 km/h. The research results show that， compared to the conventional bogie， the PMD bogie exhibits a less complex flow field structure with smoother airflow. Although the total snow accumulation in the PMD bogie region is higher， after accounting for the snow adhered to the motor， the overall snow accumulation is reduced by 23.83%， and the snow accumulation on the brake calipers is reduced by 78.368%. Thus， it can be concluded that a significant portion of the snow in the PMD bogie area adheres to the surface of the permanent magnet direct-drive motor， with relatively little snow accumulation on other components， especially the brake calipers. Considering that the heat generated by the motor during operation can melt the snow on its surface， the snow accumulations on the motor surface. Based on these properties， the direct-drive configuration of the intercity EMU bogie offers a notable advantage in mitigating snow accumulation and icing issues.

Abstract:Aiming at the problem that some key dynamical states of tractor semi-trailer cannot be measured and the values of sensors are interfered by random factors such as engine vibration noise， an improved particle filter is proposed to estimate the dynamical states of the driving tractor semi-trailer in real-time. This paper establishes a 17 degrees of freedom dynamical model of tractor semi-trailer first. By combining the particle filter principle and the adaptive genetic algorithm to enhance the particle diversity， the piecewise proposal distribution function is designed， and the systematic resampling method is used to suppress the particle regression. The in-time and accurate estimation of longitudinal speed， lateral speed， yaw rate， and other states of tractor semi-trailer was realized. A hardware-in-the-loop （HIL） simulation test platform was built to verify the algorithm under different conditions. The testing results show that compared with the unscented particle filter algorithm， the improved particle filter algorithm proposed in this paper can realize the state estimation of the whole vehicle under both ideal and random noise environments， and has higher estimation accuracy.

Abstract:To address the issue that the deep-sea mining vehicle is vulnerable to time-varying external disturbances caused by some factors such as dynamic ocean currents when operating autonomously on the seabed， and to further improve the motion control accuracy of deep-sea mining vehicle， an inner and outer loop control method of deep-sea mining vehicle based on hybrid LQR/H∞ and GPC is proposed. Based on the error kinematic model of the deep-sea mining vehicle that considers time-varying external disturbances， the LQR and H∞ control methods are combined to construct the azimuth outer loop controller， which enables the controller to possess both the ability of fast tracking of linear optimal control methods and strong robustness. The speed inner loop controller is constructed by combining the GPC speed controller based on the track’s longitudinally kinematics model and the deep-sea mining vehicle’s dynamic model， so that the controller can ensure real-time solutions while considering the dynamic characteristics of the deep-sea mining vehicle. The simulation results show that compared with the commonly used MPC controller in the lateral controller and the traditional PID controller in the longitudinal controller， the proposed method not only has higher lateral and longitudinal control accuracy， but also shows stronger robustness to time-varying external disturbances.

Abstract:To address the noise issue in experimentally acquired body pressure distribution data， this study proposes a convolutional autoencoder-based data reconstruction method to enhance data quality and usability. First， the body pressure distribution data is normalized. And Gaussian noise is added to construct the training set. A convolutional autoencoder model is designed and used for feature extraction and denoising. Subsequently， experimentally collected body pressure distribution data is utilized as the test set to evaluate the accuracy and stability of the reconstruction results. Experimental results demonstrate that the model achieves a mean relative error of 0.010 with a standard deviation of 0.018 across 98 test samples， indicating high accuracy and stability. Finally， the trained model is applied to process experimentally collected body pressure data， revealing the variation patterns of pressure distribution metrics with seat positions.

Abstract:In the context of parallel collaborative lane-changing control for drivers and intelligent systems， issues such as human-machine conflicts and driving discomfort arise due to frequent or substantial changes in the control authority allocation between humans and machines. To solve these problems， this paper proposes a lateral human-machine driving weight allocation strategy that combines pre-allocation with real-time allocation to achieve a reasonable distribution of vehicle control. Initially， to characterize the driving style of the driver， a single-point preview driver model is constructed， which includes decision functions for lateral preview error and lateral acceleration. Concurrently， a model predictive controller （MPC） is established as a co-driving control system， and a vehicle lane-changing trajectory is designed based on a quintic polynomial. Subsequently， a pre-allocation method for driving rights is designed， incorporating style coefficients， preview time， and road adhesion coefficients. Criteria for real-time allocation of weights are designed based on risk level and human-machine conflict measures， with adjustments introduced to prevent frequent changes in weights. Joint simulation results indicate that when human-machine intentions are aligned， this strategy significantly reduces the driver's burden. When driving risk is high， control weights shift towards the system， allowing timely intervention to ensure traffic safety. When human-machine intentions are inconsistent， and driving risk is low but human-machine conflict is high， it is sure that control is transferred to the driver at a fixed value， allowing the vehicle to operate according to the driver's intent， and the overall control effect is superior to fixed-weight control strategies. Driver-in-the-loop platform tests show that when drivers adapt to moderate intervention by the control system， this strategy can provide personalized lane-changing assistance for drivers of different styles.

Abstract:The light condition of driving at night is poor， and the target is easily blocked， which makes it difficult for the detection algorithm to accurately determine the edges and shapes of targets. In addition， in the process of vehicle moving， fuzzy sense is easily generated on the captured objects， resulting in the difficulty of feature extraction of targets. To address these issues， this paper proposes an improved object detection algorithm for night-time driving based on YOLOv8n. Firstly， the DCN model is introduced into the C2f module and improved into the DCN_CSP2 module， which is used to replace the C2f module in the Backbone. This enhances the algorithm to capture the shape and edge information of target objects more accurately， improves the feature extraction capability， and reduces the computational burden. Secondly， the DWConv module is specifically introduced into the Neck to reduce the number of model parameters while maintaining computational performance， and improve the computational efficiency and achieve the lightweight of the model. Additionally， to address the issue of the original NMS algorithm potentially causing the loss of important targets when they are partially obscured， a decay function based on the overlap size between the candidate detection boxes and the reference box is introduced. This makes important targets more likely to be retained and participate in the subsequent NMS process， thereby improving the detection performance of the model. Analysis results show that compared with YOLOv8n， the improved algorithms achieve a 6.2% increase in mAP@50 and a 5.7% increase in mAP@50：95 on the rmsw_5k_night dataset， and reduce the computational burden and the number of model parameters， achieving a balance between model lightweight and high performance. The improved algorithm effectively enhances the detection capability for night-time targets， and it lays a solid foundation for the algorithm to be applied to the terminal devices.

Abstract:Accurately identifying the lane change intention of vehicles is a key strategy for improving the reliability of driving assistance systems and ensuring road safety. A novel method that combines convolutional neural network （CNN） and gated recurrent unit （GRU）， integrated with a logic judgment mechanism， was proposed to effectively recognize the lane change intentions of vehicles. First， test data from twenty volunteers were recorded using a driving simulator including three categories： left lane change， right lane change， and straight driving. The data was used to construct a sample set of lane change intention. Secondly， a CNN-GRU model was built using vehicle driving characteristics and driver behavior data， with the CNN layer being employed to extract features as input to the GRU layer. Finally， a logic judgment layer was integrated into the intention recognition network to address the temporal dependencies of lane change intentions by setting probability thresholds. To validate the validity of the method in this study， the model was compared and analyzed with a CNN that was integrated with long short-term memory （LSTM） and GRU. The results show that the proposed model achieved recognition accuracies of 98.5% for left lane changes， 96.7% for right lane changes， and 95.2% for straight driving， demonstrating higher accuracy compared with other models.

Abstract:To investigate the influence of forming outer circle grinding process parameters on the surface quality of 18CrNiMo7-6 gear steel， and determine the optimal combination of process parameters， this paper uses universal tool grinder and CBN forming grinding wheel to process a V-shaped notch by forming grinding. Taking the workpiece speed， the grinding wheel speed and the grinding wheel radial feed speed as the test variables； the three-dimensional surface roughness amplitude parameters： surface roughness， root mean square roughness， surface skewness and surface kurtosis are used as the evaluation process indicators of surface quality. Single-factor experiments and orthogonal experiments are conducted separately. The experimental results indicate that as workpiece speed increases， surface roughness， root mean square roughness， and surface skewness all decrease first and then increase， while surface kurtosis gradually decreases； as grinding wheel speed increases， surface roughness and root mean square roughness also decrease first and then increase， while surface skewness and surface kurtosis increase first and then decrease； as grinding wheel radial feed speed increases， both surface roughness and root mean square roughness gradually increase， while surface skewness and surface kurtosis first increase and then decrease. The degree of influence of the three variables on the three-dimensional surface roughness of the workpiece， ranked from greatest to least， is： grinding wheel speed， grinding wheel radial feed speed， and workpiece speed. The optimal parameter combination identified from the comprehensive single-factor experiment and orthogonal experiment results is as follows： a workpiece speed of 900 r/min， a grinding wheel speed of 5 000 r/min， and a grinding wheel radial feed speed of 0.15 mm/min.

Abstract:Alumina ceramics are widely used in the field of electronic packaging because of their excellent dielectric properties and stable physical and chemical properties. However， the large crack tendency of its weld reduces the joint performance and greatly limits the application of ceramic joints. In this paper， a femtosecond laser is used to weld alumina ceramics. The effects of laser beam scanning trajectory， laser power， and scanning speed on weld crack characteristics and joint properties are studied. A classification method of weld grade based on average crack width is proposed. The results show that compared with ellipse， helix， vertical 8 characters， and horizontal 8 characters， sinusoidal scanning laser welding alumina ceramics can obtain smaller average crack width and higher shear force. Increasing the laser power and decreasing the scanning speed is beneficial to reduce the average crack width， increase the weld penetration， and then increase the shear force of the joint. The maximum shear force reaches 1980 N， which is about 61% of the base material shear force. A prediction model of joint shear force based on the average crack width and penetration depth of the weld is established， and the model’s accuracy is proved by verification experiments. The minimum deviation between the predicted value and the measured value is only 0.45%. The research results provide technical guidance and theoretical support for high-performance welding of ceramics.

Abstract:The mechanism of nano-grinding of single crystal silicon carbide （SiC） with lattice defects remains unclear. A molecular dynamics simulation system is used to study the nano-scratching mechanism of single crystal SiC with lattice defects. The simulation model including diamond abrasive grains and 4H-SiC workpieces with different lattice defects is built. The molecular dynamics simulation results reveal the effects of different defect types on key parameters such as interatomic potential energy， temperature， stress and machining performance. It is found that vacancy defects lead to instability in the interatomic potential energy of the workpiece， which in turn results in increasing the temperature of the workpiece up to 671 K after scribing， while dislocation defects show relative stability. During nano-scratching， crystals with dislocation defects exhibit the highest average paradigm equivalent stress of 5.29 GPa， while crystals with vacancy defects exhibit the lowest stress of 5.07 GPa， which suggests that vacancy defects reduce the yield strength and favour the removal of atoms， whereas dislocation defects increase the yield strength and impede the removal of atoms. Furthermore， vacancy defects inhibited dislocation nucleation and reduced the thickness of the damage layer， whereas dislocation defects led to significant dislocation formation and a deeper damage layer.

Abstract:Aiming at the problem of project duration extension caused by chain rework due to functional inspection in large industrial components assembly process， a complex assembly process scheduling problem optimization model considering functional fault rework is established， and an integer programming model is established with the objective function of minimizing project duration expectation. Based on the support vector machine quality defect probability prediction model and bayesian network， the mapping relationship between personnel allocation decision and posterior rework probability is established. A hierarchic genetic tabu search algorithm （HGA-TS） is designed. The upper layer optimizes the job execution sequence based on the job list topology sequence， the lower layer optimizes the personnel allocation based on random key， and then converts it into resource stream coding local search optimization to generate proactive scheduling plan. The experimental results of the model show that giving priority to high-level resources for assembly jobs with high posterior probability can reduce the probability of functional fault rework. The algorithm comparison experiment proves the effectiveness of HGA-TS in solving this problem.

Abstract:For the reliability analysis problem characterized by a single failure mode， cognitive uncertainty， and “black-box” models， an active learning reliability analysis method based on evidence theory is proposed. This method efficiently and accurately determines the credibility and verisimilitude of structures. It handles cognitive uncertain variables using evidence theory， initiates initial training sample construction for a Kriging model， and combines optimization methods with active learning to search for optimal training samples across the entire input variable space. This approach refines the Kriging model chronically with optimal training samples， replacing the functional function with the Kriging model to predict unknown points for credibility and verisimilitude calculation of the structure. By integrating optimization methods with active learning， the method relaxes constraints on candidate sample locations during traditional training sample search， thereby identifying training samples that better enhance the Kriging model’s correction effects and improve the efficiency and success rate of Kriging model construction. Numerical examples demonstrate the method’s computational effectiveness and its application to the reliability analysis of vehicle frontal collisions.

Abstract:To address the problem of incompatibility between different elements， the paper introduces a “trial-correction” displacement interpolation scheme. This scheme is utilized to construct a four-node quadrilateral plane element that incorporates a drilling degree of freedom. The “trial-correction” interpolation-based four-node quadrilateral plane element takes translational displacements and the drilling degree of freedom as nodal parameters， and higher-order interpolation functions are employed to approximate the displacement field. Firstly， bi-linear interpolation is used to trail the displacement fields. And then， according to the deviation of the drilling degree of freedom， the displacement fields are corrected with bi-cubic interpolation. The convergence of the “trial-correction” interpolation-based four-node quadrilateral plane element is proved by the patch test， and its performance is further verified by three examples. The numerical results show that the “trial-correction” interpolation-based four-node quadrilateral plane element has a high convergence rate， a high numerical accuracy， and can be compatible with beam elements for convenient mix-element modeling. Moreover， the “trial-correction” interpolation method is parameterized and has good extendibility， which lays a foundation for future research about other elements with true rotation angle.

Abstract:Clarifying the electric field distribution characteristics of mechanical insulated rail joints in high-speed railway stations under working conditions is the key to solving the insulation failure problem. Based on the electrostatic field finite element method， the electric field distribution of the intact insulated rail joint under steady-state voltage and transient overvoltage is calculated considering the thickness， the material of the insulated rail joint and the power supply mode， respectively， and the influence of different working conditions on the electric field distribution of the insulated rail joint is also analyzed. The possible defects of the insulated rail joint are analyzed. The effect of bubbles， air gap and carbonization on the electric field distribution of the insulated rail joint is studied. The results show that the overall electric field distribution of the insulated rail joint is uneven. Partial discharges may occur at the top surface of the insulated rail joint under transient overvoltage. The thickness and the power supply mode have a certain effect on the electric field distribution of the insulated rail joint， and the effect of direct supply is larger than that of auto-transformer （AT） power supply mode. The influence of bubbles on the electric field distribution is related to their locations， and air breakdown is prone to occur around bubbles， causing partial discharge. Carbonization has a greater effect on the electric field distribution of the insulated rail joint. The carbonization depth changes the maximum value of the local electric field distribution intensity of the insulated rail joint， and the influence of carbonization depth on the electric field intensity is greater in the top region than in the waist and bottom areas.

Abstract:An elevation measurement method via the fusion of vision and barometric sensors is proposed to achieve convenient and accurate measurement of the elevations of operators during aerial work. A YOLOX deep neural network is constructed to detect the aerial operators and their positions in the pictures of the aerial work site. The changes of real altitude in the relatively short period of climbing are measured via a barometric sensor. Using the barometric elevation measurement and vision detection results， the support vector regression （SVR） is applied to construct and update the regressive model between the image position and the actual elevation of the operator， and the high accuracy elevation measurement results are obtained through the regressive model. In real-world aerial work experiments， the proposed method is compared with the direction elevation measurement using a single barometric sensor and the differential elevation measurement by two barometric sensors. The measurement errors， in the mean absolute error as well as in the root-mean squares-error， of the proposed method are all lower than 0.2 m， and are superior to the two rival methods. The performances of the proposed methods are achieved by fusing the vision detection and the barometric elevation measurement， which overcomes the serious signal shifting and degradation of measurement accuracy of the barometric sensors， and also avoids the laborious calibration procedure of the vision detection system. The proposed method’s elevation measurement accuracy satisfies the requirement on the operator in aerial work， and it is easy to apply in work sites without putting extra burdens on the operators， making the method a practical choice for operator safeguarding in aerial work.

Abstract:Aiming at the problems of serious cross influence， control difficulty and poor transient performance of the two output branches of the single inductor dual output Buck-Boost converter （SIDO Buck-Boost） with non-minimum phase characteristics， a control strategy based on the extended state observer （ESO） combined with the differential flatness based control （DFBC） of the main circuit and the improved dual closed-loop active disturbance rejection controller （ADRC） of the branch circuit is proposed. Firstly， based on the theory of differential flatness， a differential flatness controller is designed in the main circuit control， and error feedback is provided for the differential flatness system. ESO is designed to observe the disturbance term of the main circuit， and the observed state variables are fed back to the differential flatness controller. Secondly， to solve the problem of branch coupling and right half plane zero， an improved dual closed-loop ADRC is designed to decouple the system. The current inner loop selects ADRC based on model compensation and feedforward compensation， and the voltage outer loop selects ordinary ADRC. Then， Lyapunov theory is used to prove the stability of the system. Finally， a simulation model was built on the Matlab/Simulink platform， and an experimental platform was built based on HIL. The simulation and experimental results show that the proposed control strategy reduces the cross influence between the two output branches， solves the problem of non-minimum phase system control difficulty， and improves the transient response performance of the system.

Abstract:Silicone rubber materials are commonly used as insulation materials under high-pressure conditions due to their excellent insulation properties. The breakdown field strength is an important electrical performance index， and there is a complex nonlinear relationship between the breakdown field strength and the material formula. Based on this， an efficient evaluation model based on genetic algorithm （GA） optimized extreme gradient boosting （XGBoost） algorithm is proposed. The model combines GA and XGBoost， and uses temperature， relative content of masterbatch， diameter of Al（OH）3 micropowder， relative content of Al（OH）3 and thickness as inputs to establish an improved XGBoost model to predict the breakdown field strength. The GA algorithm automatically selects the optimal parameters during the training process of the XGBoost model. The Pearson correlation coefficient is used to analyze the influencing factors. It can be seen that the thickness and temperature are the key factors affecting the breakdown field strength， while the influence of the masterbatch content， the diameter and relative content of Al（OH）3 micropowder is relatively small. The evaluation index of the commonly used regression model is compared with the proposed model. The coefficient of determination of the model can reach 0.953， and the root-mean-square error and mean absolute error are only 0.361 kV/mm and 0.168， respectively. The results show that the GA-XGBoost model can accurately predict the breakdown fieid strength of the material， which can provide a reference for studying the properties of silicone rubber materials and optimizing the material formulation.

Abstract:To meet the demands of communication base stations， radars， and other systems for high spectral purity local oscillator signals， an ultra wideband and multi functional phase-locked loop （PLL） chip was designed and implemented based on the 130 nm SiGe BiCMOS process. An off-chip test circuit system was also designed in conjunction with the chip’s application. The digital-controlled charge pump （CP） within the PLL chip can adjust crucial parameters such as loop bandwidth and system power consumption by controlling the CP current. The wideband switchable frequency divider divides the fundamental wave signal output by voltage controlled oscillator（VCO） with different operating frequency bands and performance characteristics outside the chip in the feedback loop， achieving a locked output of the fundamental wave signal in the range of 1~5.8 GHz. At the same time， an independent frequency division system integrated within the chip further expands the locking bandwidth by dividing the VCO’s fundamental wave signal output by 1/2/4/8/16， covering the output of low-frequency signals ranging from 0.15~1 GHz below the fundamental wave signal band. Tape-out testing of this PLL chip demonstrates a phase noise of -105.8 dBc/Hz at 100 kHz within the loop bandwidth for a fundamental wave output of 2.4 GHz， with a reference spur suppression of -86.12 dBc. Powered by 3.3 V， the chip can achieve a maximum phase detection frequency of 75 MHz and operate normally between -55 °C and +85 °C， providing high spectral purity local oscillator signals.

Abstract:In response to the issues of high hardware resource consumption and slow processing speed associated with traditional digital interpolation filters， a design methodology based on operand resource reuse is proposed to enhance digital interpolation filter performance. Building upon the foundation of a polyphase digital interpolation filter， this method optimizes the filter architecture to enable the reuse of core computational resources， resulting in a significant reduction in circuit resources and power consumption. A novel architecture filter proposed in this study is prototyped verified on an FPGA platform，and comparative analyses are conducted with traditional interpolation filters， multi-channel parallel interpolation filters， and polyphase interpolation filters. The results indicate that the improved filter requires 65% fewer registers compared to the traditional structure， 73% fewer registers compared to the multi-channel parallel structure， and 28% fewer registers compared to the polyphase structure， respectively. The maximum operating clock frequency is increased by 129% compared to the traditional structure and 13.8% compared to the multi-channel parallel structure. Moreover， power consumption is lower than that of traditional structure and multi-channel paralle structure， making it more suitable for high-speed and low-power consumption applications.