Spatial-temporal combination and multi-head flow-attention network for traffic flow prediction.

Traffic flow prediction based on spatial-temporal data plays a vital role in traffic management. However, it still faces serious challenges due to the complex spatial-temporal correlation in nonlinear spatial-temporal data. Some previous methods have limited ability to capture spatial-temporal correlation, and ignore the quadratic complexity problem in the traditional attention mechanism. To this end, we propose a novel spatial-temporal combination and multi-head flow-attention network (STCMFA) to model the spatial-temporal correlation in road networks. Firstly, we design a temporal sequence multi-head flow attention (TS-MFA), in which the unique source competition mechanism and sink allocation mechanism make the model avoid attention degradation without being affected by inductive biases. Secondly, we use GRU instead of the linear layer in traditional attention to map the input sequence, which further enhances the temporal modeling ability of the model. Finally, we combine the GCN with the TS-MFA module to capture the spatial-temporal correlation, and introduce residual mechanism and feature aggregation strategy to further improve the performance of STCMFA. Extensive experiments on four real-world traffic datasets show that our model has excellent performance and is always significantly better than other baselines.

Cluster equilibrium scheduling method based on backpressure flow control in railway power supply systems.

The purpose of the study is to solve problems, i.e., increasingly significant processing delay of massive monitoring data and imbalanced tasks in the scheduling and monitoring center for a railway network. To tackle these problems, a method by using a smooth weighted round-robin scheduling based on backpressure flow control (BF-SWRR) is proposed. The method is developed based on a model for message queues and real-time streaming computing. By using telemetry data flow as input data sources, the fields of data sources are segmented into different sets by using a distributed model of stream computing parallel processing. Moreover, the round-robin (RR) scheduling method for the distributed server is improved. The parallelism, memory occupancy, and system delay are tested by taking a high-speed train section of a certain line as an example. The result showed that the BF-SWRR method for clusters can control the delay to within 1 s. When the parallelism of distributed clusters is set to 8, occupancy rates of the CPU and memory can be decreased by about 15%. In this way, the overall load of the cluster during stream computing is more balanced.

A novel RFID multi-tag anti-collision protocol for dynamic vehicle identification.

In order to obtain the information of the vehicle tags in adverse traffic conditions, we proposed a novel reservation framework named reservation to cancel idle-dynamic frame slotted ALOHA (RTCI-DFSA) algorithm. Firstly, the framework employed reservation mechanism to remove idle slot, and thus improve the system identification efficiency. Secondly, the vehicle information was identified by the tag serialization polling identification method. The experimental results showed that the proposed RTCI-DFSA algorithm performed better than the traditional frame slotted ALOHA (FSA) and dynamic frame slotted ALOHA (DFSA) algorithms. More specifically, the tag loss rate of the proposed framework is significantly lower than the frame length fixed and conventional dynamic vehicle identification algorithms. In addition, the experimental results demonstrated that the throughput rate of the proposed algorithm increased from 0.368 to 0.6. Besides, the identification efficiency and applicability of the proposed framework were both higher than other tag identification algorithms.

Electrothermal switching characteristics from a hydrogen-bonded polymer network structure in cholesteric liquid crystals with a double-handed circularly polarized light reflection band.

In this study, cholesteric liquid crystal (Ch-LCs) composite with a double-handed circularly polarized light reflection band that contains hydrogen-bonded (H-bonded) polymer network was fabricated by a polymer template method, in which the environmental sensitivity and reversibility of hydrogen bond and memory effect of the polymer template were incorporated into the polymer stabilized cholesteric liquid crystal (PSCLCs) film. The thermal and electrical tunability of the Ch-LCs composite before and after irradiation were investigated. It has been demonstrated that reflection characteristics of the Ch-LCs composite derived from H-bonded chiral monomer (HBCM) after irradiation exhibited strong temperature sensitivity with temperature change, which we believe is due to that helical twisting power value of both HBCM and cholesteryl additives increased with an increasing temperature. Additionally, the reflective wavelength of the Ch-LCs composite before and after irradiation can be electrically switched to reflect green from the initial state reflecting a red color. The modulating mechanism is due to Helfrich deformation in which the tilt of helices axis in the Ch-LCs composite was induced when a voltage was applied. The technique developed in this study has great applications such as tunable lasers, optical sensors and LCs displays.