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The haze-prone areas are usually places with limited transmission line corridors and large power loads. The performance of transmission lines is under threat of haze. The haze particulates around the transmission lines would be charged and affect the electric field near high voltage direct current (HVDC) transmission lines. According to the influence mechanism of haze on ionized field, the electric performance of HVDC transmission lines under haze weather is discussed. In the paper, an improved meshless local Petrov-Galerkin method (MLPG) is proposed to investigate the distribution of ionized electric field, and example of an actual transmission line is studied to verify the validation of the proposed method at first. It is proved that the proposed method agrees well with the measurements. Then the ionized field distribution under different haze weather levels, as well as influenced factor, is discussed. Results indicate that the ionized electric field and ion current density on the ground would increase under haze weather, but with similar trends to good weather condition. Meanwhile, the haze weather levels have greater influence on the ionized electric field than ion current density, where the increase of corona and space charge are the main reasons.

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At present, the train-ground communication system based on the wireless communication protocol is a very important component of communication-based train control (CBTC) systems in intelligent transportation. Its information security is worthy of attention. In order to guarantee the security of the train-ground communication system, this paper proposes an improved AdaBoost multi-classification intrusion detection method based on the n-gram model. First, the n-gram model is used to model the state transitions of the IEEE 802.11 protocol. Then, a typical normal behavior set and typical abnormal behavior sets are obtained by learning and they can portray typical behaviors of their respective classes. Furthermore, a similarity measure algorithm is proposed to construct AdaBoost weak classifiers, which improves the classification effect of AdaBoost algorithm. At last, an AdaBoost multi-classification algorithm is presented to detect and identify the attacks. Experiments prove that the algorithm can effectively detect and distinguish attack types in the train-ground communication system.