Vibration Control of the Rail Grinding Vehicle with Abrasive Belt Based on Structural Optimization and Lightweight Design

As a new grinding and maintenance technology,rail belt grinding shows significant advantages in many applications The dynamic characteristics of the rail belt grinding vehicle largely determines its grinding performance and service life.In order to explore the vibration control method of the rail grinding vehicle with abrasive belt,the vibration response changes in structural optimization and lightweight design are respectively analyzed through transient response and random vibration simulations in this paper.Firstly,the transient response simulation analysis of the rail grinding vehicle with abrasive belt is carried out under operating conditions and non-operating conditions.Secondly,the vibration control of the grinding vehicle is implemented by setting vibration isolation elements,optimizing the structure,and increasing damping.Thirdly,in order to further explore the dynamic characteristics of the rail grinding vehicle,the random vibration simulation analysis of the grinding vehicle is carried out under the condition of the horizontal irregularity of the American AAR6 track.Finally,by replacing the Q235 steel frame material with 7075 aluminum alloy and LA43M magnesium alloy,both vibration control and lightweight design can be achieved simultaneously.The results of transient dynamic response analysis show that the acceleration of most positions in the two working conditions exceeds the standard value in GB/T 17426-1998 standard.By optimizing the structure of the grinding vehicle in three ways,the average vibration acceleration of the whole car is reduced by about 55.1%from 15.6 m/s^(2) to 7.0 m/s^(2).The results of random vibration analysis show that the grinding vehicle with Q235 steel frame does not meet the safety conditions of 3σ.By changing frame material,the maximum vibration stress of the vehicle can be reduced from 240.7 MPa to 160.0 MPa and the weight of the grinding vehicle is reduced by about 21.7%from 1500 kg to 1175 kg.The modal analysis results indicate that the vibration control of the grinding vehicle can be realized by optimizing the structure and replacing the materials with lower stiffness under the premise of ensuring the overall strength.The study provides the basis for the development of lightweight,diversified and efficient rail grinding equipment.

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide.This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions.The infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good performance.The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)algorithm.This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully.To avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller.The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers.Moreover,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Attention Mechanism-Based Method for Intrusion Target Recognition in Railway

The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.

Multimode Design and Analysis of an Integrated Leg-Arm Quadruped Robot with Deployable Characteristics

To improve locomotion and operation integration, this paper presents an integrated leg-arm quadruped robot(ILQR) that has a reconfigurable joint. First, the reconfigurable joint is designed and assembled at the end of the legarm chain. When the robot performs a task, reconfigurable configuration and mode switching can be achieved using this joint. In contrast from traditional quadruped robots, this robot can stack in a designated area to optimize the occupied volume in a nonworking state. Kinematics modeling and dynamics modeling are established to evaluate the mechanical properties for multiple modes. All working modes of the robot are classified, which can be defined as deployable mode, locomotion mode and operation mode. Based on the stability margin and mechanical modeling, switching analysis and evaluation between each mode is carried out. Finally, the prototype experimental results verify the function realization and switching stability of multimode and provide a design method to integrate and perform multimode for quadruped robots with deployable characteristics.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

Low-temperature chemistry in plasma-driven ammonia oxidative pyrolysis

Ammonia is gaining increasing attention as a green alternative fuel for achieving large-scale carbon emission reduction. Despite its potential technical prospects, the harsh ignition conditions and slow flame propagation speed of ammonia pose significant challenges to its application in engines. Non-equilibrium plasma has been identified as a promising method, but current research on plasma-enhanced ammonia combustion is limited and primarily focuses on ignition characteristics revealed by kinetic models. In this study, low-temperature and low-pressure chemistry in plasma-assisted ammonia oxidative pyrolysis is investigated by integrated studies of steady-state GC measurements and mathematical simulation. The detailed kinetic mechanism of NH_(3) decomposition in plasma-driven Ar/NH_(3) and Ar/NH_(3)/O_(2) mixtures has been developed. The numerical model has good agreements with the experimental measurements in NH_(3)/O_(2) consumption and N_(2)/H_(2) generation, which demonstrates the rationality of modelling. Based on the modelling results, species density profiles, path flux and sensitivity analysis for the key plasmaproduced species such as NH_(2), NH, H_(2), OH, H, O, O(^(1)D), O_(2)(a^(1)△_(g)), O_(2)(b^(1)∑_(g)^(+)), Ar^(*), H^(-), Ar^(+), NH_(3)^(+), O_(2)^(-) in the discharge and afterglow are analyzed in detail to illustrate the effectiveness of the active species on NH_(3) excitation and decomposition at low temperature and relatively higher E/N values. The results revealed that NH_(2), NH, H as well as H_(2) are primarily generated through the electron collision reactions e + NH_(3)→ e + NH_(2)+ H, e + NH_(3)→ e + NH + H_(2) and the excited-argon collision reaction Ar^(*) + NH_(3)+ H → Ar + NH_(2)+ 2H, which will then react with highly reactive oxidative species such as O_(2)^(*), O^(*), O, OH, and O_(2) to produce stable products of NOx and H_(2)O. NH_(3)→ NH is found a specific pathway for NH_(3) consumption with plasma assistance, which further highlights the enhanced kinetic effects.

Effect of temperature and time on the precipitation ofκ-carbides in Fe-28Mn-10Al-0.8C low-density steels:Aging mechanism and its impact on material properties

In low-density steel,κ-carbides primarily precipitate in the form of nanoscale particles within austenite grains.However,their precipitation within ferrite matrix grains has not been comprehensively explored,and the second-phase evolution mechanism during aging remains unclear.In this study,the crystallographic characteristics and morphological evolution ofκ-carbides in Fe-28Mn-10Al-0.8C(wt%)low-density steel at different aging temperatures and times and the impacts of these changes on the steels’microhardness and properties were comprehensively analyzed.Under different heat treatment conditions,intragranularκ-carbides exhibited various morpho-logical and crystallographic characteristics,such as acicular,spherical,and short rod-like shapes.At the initial stage of aging,acicularκ-carbides primarily precipitated,accompanied by a few spherical carbides.κ-Carbides grew and coarsened with aging time,the spherical carbides were considerably reduced,and rod-like carbides coarsened.Vickers hardness testing demonstrated that the material’s hardness was affected by the volume fraction,morphology,and size ofκ-carbides.Extended aging at higher temperatures led to an increase in carbide size and volume fraction,resulting in a gradual rise in hardness.During deformation,the primary mechanisms for strengthening were dislocation strengthening and second-phase strengthening.Based on these findings,potential strategies for improving material strength are proposed.

The effects of inner electrode shape on the performance of dielectric barrier discharge reactor for oxidative removal of NO and SO_(2)

Seagoing vessels are responsible for more than 90%of global freight traffic,but meanwhile,emission pollutants(NO_(x)and SO_(x))of seagoing vessels also cause serious air pollution.Nonthermal plasma(NTP)combined with wet scrubbing technology is considered to be a promising technology.In order to improve the oxidation efficiency and energy efficiency of the NTP reactor,the screw and rod inner electrodes of dielectric barrier discharge(DBD)reactor were investigated.To analyze the mechanism,the optical emission spectra(OES)of NTP were measured and numerical calculation was applied.The experiment results show that the NO oxidation removal efficiency of screw electrode is lower than that of rod electrode.However,the SO_(2)removal efficiency of screw electrode is higher.According to the OES experiment and numerical calculation,the electric field intensity of the screw electrode surface is much higher than that of the rod electrode surface,and it is easier to generate N radicals to form NO.For the same energy density condition,the OH radical generation efficiency of the screw electrode reactor is similar to that of the rod electrode,but the gas temperature in the discharge gap is higher.Therefore,the SO2 oxidation efficiency of the thread electrode is higher.This study provides guidance for the optimization of oxidation efficiency and energy consumption of DBD reactor.

Effects of plasma spraying process on microstructure and mechanical properties of Cr_(2)AlC/410 composite coatings

To investigate the influences of Cr_(2)AlC mass fraction and supersonic plasma spraying process on the microstructure and mechanical properties of Cr_(2)AlC reinforced 410 stainless steel composite coatings,the coatings containing different mass fractions of Cr_(2)AlC were prepared and investigated.The composite coating exhibited low porosity and high adhesion strength.The addition of Cr_(2)AlC significantly enhanced the hardness of the composite coatings through particle strengthening.However,when the mass fraction of Cr_(2)AlC was 20%,the aggregation of Cr_(2)AlC resulted in a strong decrease in the coating preparation efficiency,as well as a decline in adhesion strength.In the supersonic plasma spraying process,the Ar flow rate mainly influenced the flight velocity of the particles,while the H_(2) flow rate and the current mainly affected the temperature of the plasma torch.Consequently,all of them influenced the melting degree of particles and the quality of the coating.The lowest porosity and the highest hardness and adhesion strength could be obtained when the Ar flow rate is 125 L/min,the H_(2) flow rate is 25 L/min,and the current is 385 A.

Torque effect on vibration behavior of high-speed train gearbox under internal and external excitations

The high-speed train transmission system,experiencing both the internal excitation originating from gear meshing and the external excitation originating from the wheel-rail interaction,exhibits complex dynamic behavior in the actual service environment.This paper focuses on the gearbox in the high-speed train to carry out the bench test,in which various operat-ing conditions(torques and rotation speeds)were set up and the excitation condition covering both internal and external was created.Acceleration responses on multiple positions of the gearbox were acquired in the test and the vibration behavior of the gearbox was studied.Meanwhile,a stochastic excitation modal test was also carried out on the test bench under different torques,and the modal parameter of the gearbox was identified.Finally,the sweep frequency response of the gearbox under gear meshing excitation was analyzed through dynamic modeling.The results showed that the torque has an attenuating effect on the amplitude of gear meshing frequency on the gearbox,and the effect of external excitation on the gearbox vibration cannot be ignored,especially under the rated operating condition.It was also found that the torque affects the modal param-eter of the gearbox significantly.The torque has a great effect on both the gear meshing stiffness and the bearing stiffness in the transmission system,which is the inherent reason for the changed modal characteristics observed in the modal test and affects the vibration behavior of the gearbox consequently.

Research on the evolution law of dynamic performance of CR400BF EMU train based on stochastic dynamics simulation

Purpose–This paper aims to obtain the evolution law of dynamic performance of CR400BF electric multiple unit(EMU).Design/methodology/approach–Using the dynamic simulation based on field test,stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers were tested.Stiffness,damping coefficient,friction coefficient,track gauge were taken as random variables,the stochastic dynamics simulation method was constructed and applied to research the evolution law with running mileage of dynamic index of CR400BF EMU.Findings–The results showed that stiffness and damping coefficient subjected to normal distribution,the mean and variance were computed and the evolution law of stiffness and damping coefficient with running mileage was obtained.Originality/value–Firstly,based on the field test we found that stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers subjected to normal distribution,and the evolution law of stiffness and damping coefficient with running mileage was proposed.Secondly stiffness,damping coefficient,friction coefficient,track gauge were taken as random variables,the stochastic dynamics simulation method was constructed and applied to the research to the evolution law with running mileage of dynamic index of CR400BF EMU.

Temperature field calculation of rail flash welding

The forging stage of rail flash welding has a decisive influence on joint strength,and the study of the temperature distribution in the process has an important role in further improving joint strength.In this paper,three calculation methods for the temperature field are given.First,the finite element model of the temperature field before forging rail flash welding is established by using the transient heat module of Ansys software and verified by infrared temperature measurement.Second,the temperature distribution of different parts of the rail before flash welding is obtained by using infrared thermal imaging equipment.Third,Matlab software is used to calculate the temperature of the non-measured part.Finally,the temperature distribution function along the rail axis is fitted through the temperature measurement data.The temperature distribution before the top forging of the rail flash welding can be used to analyze the joint and heat-affected zone organization and properties effectively and to guide the parameter setting and industrial production.

Neural Network Predictive Control of Variable-pitch Wind Turbines Based on Small-world Optimization Algorithm

通过将混沌映射用于产生初始节点集和进行算子构造,提出一种新的基于实数编码的混沌小世界优化算法。采用4种算法对多例复杂函数的优化问题进行仿真试验,表明所提算法具有能够有效避免陷入局部极小值、快速搜索到最优值的能力。将上述方法应用于变桨距风电机组启动并网时的转速控制,提出一种基于混沌小世界优化算法的神经网络预测控制策略,其预测模型由基于现场数据的神经网络模型建立。仿真与实际测试结果表明,该系统可以根据风速扰动提前预测电机的转速变化,使控制器超前动作,保证系统输出跟踪参考轨迹的方向稳步改变,确保风电机组平稳并网。

Adaptive Fuzzy Dynamic Surface Control of Flexible-Joint Robot Systems With Input Saturation

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm.The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model.To adaptively adjust the priorities of path tracking accuracy and vehicle stability,an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function.An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions.To ensure vehicle stability,the sideslip angle,yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame.It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and largecurvature conditions.

Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

Locomotion Optimization and Manipulation Planning of a Tetrahedron-Based Mobile Mechanism with Binary Control

Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degree of freedom(DOF), which is infeasible to the optimization of binary control multi-DOF system. A novel optimization method using for the locomotion and manipulation of an 18 DOFs tetrahedron-based mechanism called 5-TET is proposed. The optimization objective is to realize the required locomotion by executing the least number of struts.Binary control strategy is adopted, and forward kinematic and tipping dynamic analyses are performed, respectively.Based on a developed genetic algorithm(GA), the optimal number of alternative struts between two adjacent steps is obtained as 5. Finally, a potential manipulation function is proposed, and the energy consumption comparison between optimal 5-TET and the traditional wheeled robot is carried out. The presented locomotion optimization and manipulation planning enrich the research of tetrahedron-based mechanisms and provide the instruction to the successive locomotion and operation planning of multi-DOF mechanisms.

The Simulation Study of HEV Under Electric Assist Control Strategy （EACS）

Hybrid Electric Vehicle (HEV) becomes a popular research project all over the world because of its high efficiency and low emissions. HEV is driven by Internal Combustion (IC) engine and electric motor, and its powertrain is more complex than conventional vehicle. Therefore, the complexity of a HEV powertrain demands a reliable control strategy be developed to guarantee stable and consistent operation. In this paper, EACS is proposed which takes IC engine as a main power unit and the electric motor as an assistant one. Because the fuel consumption of HEV mainly depends on IC engine, EACS restricts the IC engine to operate under the condition with favorable fuel economy. In the paper several control parameters are described such as the maximum torque, the lowest and highest desired battery state of charge (SOC), whose values will affect the performance of HEV remarkably. In addition, the test models of power units for HEV, mathematical models of transmission and vehicle dynamics for HEV are established. A forward-facing simulation software (i.e. driver-to-wheel) based on MATLAB/Simulink is developed to test the performance of EACS and determine the proper value of the control parameters. The simulation results and conclusion are also shown in the paper.

Design of adaptive intuitionistic fuzzy controller for synchronisation of uncertain chaotic systems

This study presents the synchronisation between two non-linear Lorenz chaotic systems using an adaptive intuitionistic fuzzy controller(AIFC).This problem is solved in the presence of external disturbances and uncertainties.Two classes of Lorenz systems have been formulated one as the master and the other as the slave.The master and slave systems have different initial conditions.Under different initial conditions,both systems diverge.This is called the butterfly effect that rules the behaviour of chaotic systems.AIFC is chosen as a different method that has not been used before to synchronise the chaotic systems.The scaling factors of the inputs and the output of the intuitionistic fuzzy controller(IFC)are tuned using the artificial bee colony(ABC)algorithm depending on a specified objective function.Then IFC stability is studied when it is applied to synchronise the Lorenz system.The simulation results achieved to assess the adequacy of the proposed control and to exhibit its dependability to synchronise the Lorenz system.