A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge-Cloud Computing.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

Identification and analysis of ship waiting behavior outside the port based on AIS data.

Port congestion caused by ship detention is an important reason for the obstruction of the shipping supply chain. In this paper, a method of excavating the ship waiting behavior outside the port is proposed based on the automatic identification system (AIS) data and considering uncontrollable factors. Firstly, combined with the analysis of ship navigation behavior, the speed threshold of the ship waiting outside the port is defined through average speed. Secondly, the range of ships berthing in the port is distinguished, and the different waiting behavior of ships are clarified. Finally, the situation of different types and sizes of ships waiting outside the port is analyzed. The paper found that ships of different types and sizes have distinctive clustered waiting behavior, and there are significant differences in waiting time. At the ship type, bulk cargo ship have the highest number among ships waiting outside the port, followed by tanker and container ship; at the ship scale, basically, the number of ships waiting outside the port decreases with the increase of the scale, but the average waiting time is longer. It has an excellent practical promotion value for the application of AIS data and development of shipping.

One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.

Textured Sr2Sc0.1Nb0.1Co1.5Fe0.3O6-2δ Thin Film Cathodes for IT-SOFCs.

Reducing the operating temperature of solid oxide fuel cells (SOFCs) to intermediate (650⁻850 °C) or even lower levels (400⁻650 °C) is an important practical requirement. However, the main obstacle to lowering the operating temperature is the poor oxygen reduction reaction (ORR) activity on the cathode side and, therefore, it is essential to explore cathode materials with good ORR activity in these temperature ranges. In this work, we investigated the possibility of using Sr2Sc0.1Nb0.1Co1.5Fe0.3O6-2δ (SSNCF) as a suitable intermediate temperature cathode material. SSNCF thin films with different orientations were prepared using the pulsed laser deposition technique, and the relationship of the surface chemical states and ORR activity was discussed in terms of crystallographic orientation. The results showed that the SSNCF/YSZ grown along the [110] direction exhibited superior ORR activity compared to the SSNCF/SDC/YSZ thin film electrode grown along the [100] direction. This was explained by the variation in the Sr-surface enrichment and cobalt ion oxidation state using X-ray photoemission spectroscopy.