The transmission power of mobile terminals is one of the key factors that affect the distances between 5G-R base stations.Selecting a reasonable terminal transmission power parameter is of great significance for expanding the distances between base stations,making full use of the resources in existing base station sites along the railway lines,and saving the deployment costs for 5G-R base stations.First of all,based on the 5G-R service and function requirements,the bandwidth requirements of different types of mobile users are obtained.Then,based on the uplink demand,the base station spacing of the 5G-R mobile terminal with 200 mW and 2 W transmission power is estimated by using theoretical calculation and simulation.The results show that,compared with 200 mW,the distance between 5G-R base stations is 1.79-2 times greater than that of the original 5G-R base stations when the terminal transmission power is 2 W.Using typical engineering parameters of high-speed railways,with an uplink transmission rate of no less than 1 Mb·s-1 at the edge of the community and a terminal transmission power of 2 W,the distance between 5G-R base stations in suburban open areas can reach 3.3-4.0 km(RRU on the tower).Mobile terminals should determine their appropriate power levels of uplink transmission according to their individual uplink traffic.It is suggested that the 5G-R communication module of the on-board integrated transmission equipment should use 2 W transmission power.
The existence of wireless network time delay is the key problem restricting the development of wireless communication network for high-speed train.Time delay samples are obtained by building a semi-physical network control test platform based on wireless communication.Aiming at the high complexity of time delay,a processing method combining complementary ensemble empirical mode decomposition(CEEMD)and singular value decomposition(SVD)is proposed.Sample entropy is introduced to reduce the amount of computation.Finally,LSSVM model optimized by chaotic particle swarm optimization is used to complete time delay prediction.Taking traction-brake module of high-speed train as the controlled object,a sliding mode controller based on multi-power reaching law is designed for time delay compensation.Chaotic particle swarm optimization algorithm is used to optimize the controller parameters intelligently.Integrated time and absolute error(ITAE)are improved into ITAE*for the fitness function to reduce the tracking error and improve the response speed.The results show that the sample entropy of the maximum component is reduced from'above 2.7'to'below 2.0'using CEEMD decomposition.After secondary treatment with SVD,the sample entropy of Intrinsic Mode Function 1(IMF1)component with rest of the maximum sample entropy reduced to 1.5,which effectively reduces the difficulty of prediction.After improvement,Mean Square Error(MSE),Mean Absolute Error(MAE)and Mean Absolute Percentage Error(MAPE)of delay prediction are reduced to 0.1396,0.2964 and 0.0083 respectively,and the prediction accuracy is improved.For longer time delay,ITAE*index is only 0.136％of that before compensation,and the cumulative absolute error of speed tracking is 0.696％of that before compensation.The speed and racking curve have no obvious vibration when the running state of the high-speed train changes and can quickly return to the stable state when the train encounters disturbance.The research results can provide a new approach to introducing wireless network control for high-speed trains.
In order to better coordinate the allocation of railway transport capacity,enhance the quality of passenger travel service,and objectively reflect the game relationship between railway enterprises and passengers,an optimization method of train stop schedule under elastic demand is proposed.Firstly,based on the consideration of elastic demand and strict train capacity constraints,the interrelationships among the train stop schedule,passenger travel cost,and elastic demand are analyzed,thereby formulating an optimization model of train stop schedule.Secondly,the linearization methods such as relaxation and outer approximation techniques are employed to reformulate the model into a linear programming for efficient solution.Finally,the effectiveness of the proposed method is verified by a small-scale example and a real example of Zhengzhou-Xi'an High-Speed Railway,respectively.The results show that the optimized stop schedule achieves a reasonable allocation of railway transport capacity.In comparison to the real stop schedule,the optimized one reduces 22 stops and decreases the total additional stop time of passengers by 76.14％with an increase of 0.30％in fare revenue,which realizes the consideration of both railway transportation efficiency and passenger service quality.
In response to the limitations of traditional structural configuration detection due to insufficient sample capacity in establishing comprehensive existing structural models,a neural network-based structural configuration inversion algorithm is proposed for identifying existing large-span station structures.Based on the linear elastic assumption of large-span spatial structures and the correlation between configuration deviations and differences in structural systems and stiffness,a structural configuration base vector space is constructed by superimposing equivalent random loads.Using configuration base vectors and measured configurations at sampled nodes as input,a backpropagation(BP)neural network is trained to obtain the actual overall structure configuration.The performance of the algorithm is validated and analyzed using the large-span spatial structure of the Pingshan high-speed railway station in Shenzhen as an example.The results indicate that the number of random loads and training samples are the primary factors influencing the accuracy and convergence speed of the inversion algorithm.For large-span spatial structures of the same type as the Pingshan station building,when the number of base vectors exceeds 200 and 300 sets of training samples are selected,the inversion effect with low error and fast convergence speed can be obtained.The proposed neural network-based structural configuration inversion algorithm can effectively infer the true overall configuration of the structure based on a small number of measured node configurations.
To study the influence laws of different levels of eddy current braking on rail temperature rise,based on the principle of linear eddy current braking,the theoretical formulae of eddy current braking force and rail temperature rise are derived using the law of energy conservation and the skin effect formula of conductors,with the combination of the data of braking force test and heat transfer theory.According to the theoretical formulae,the finite element method is used to establish a rail temperature rise model,and the temperature variation of different parts of the rail after a train applies emergency eddy current braking are analyzed.According to the minimum time interval between the designed train and the current train in operation of Beijing-Shanghai high-speed railway,the rail temperature variation of multiple trains under repeated eddy current braking is calculated,respectively.The results show that using the law of energy conservation to derive the theoretical formulae for eddy current braking force and the rail temperature rise not only ensures the calculation accuracy,but also simplifies the calculation process.After emergency braking of a train,the temperature rise at the top of the rail is the highest,while the temperature changes at the waist and the bottom of the rail are minimal during the braking process.When multiple trains repeatedly apply eddy current braking in the same section,the higher the braking level,the shorter the train interval,and the greater the cumulative temperature rise of rail.When the maximum service braking is repeatedly applied under the condition of minimum train interval,the maximum rail temperature rise meets the requirements of relevant standards for line maintenance.
