No abstract

No abstract

No abstract

No abstract

No abstract

Thunderstorm identification from weather radar data remains a fundamental problem in lightning nowcasting. In this study, a novel thunderstorm identification method combining the area of graupel distribution region and weather radar reflectivity is proposed. To optimize identification parameters of the identification method, the radar echoes with 312 thunderstorms in 17 weather processes in Nanjing area, China, from 2014 to 2015 were identified and tested to obtain the optimal identification parameters.The results indicate that the optimal parameter combination is to set the storm region identification reflectivity threshold at 0°C isothermal level to 30 dBZ and set the area of graupel distribution region to 2 km 2 . With such a parameter combination, the optimal identification results with the probability of detection of 91%, false alarm rate of 6.9%, and critical success index of 85.3% could be obtained. Moreover, the proposed thunderstorm identification method can also provide effective means for thunderstorm nowcasting.Plain Language Summary Thunderstorm is a kind of disastrous weather that often brings us great losses. If thunderstorms can be timely detected, we can take appropriate means to deal with them. Weather radar is considered as an efficient tool for detecting thunderstorms. It is an important problem in the application of weather radar to identify thunderstorms from radar echoes. In the present study, a new method for identifying thunderstorms using dual-polarization weather radar is proposed, which combines reflectivity with the integral area of graupel related to thunderstorm electrification to identify thunderstorms. The method is tested by 312 thunderstorms in 17 weather processes in Nanjing area, China, from 2014 to 2015. The test results show that the method has good thunderstorm identification performance.

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the corresponding disturbance rate (False Positive Rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.

Aiming at the operation and maintenance requirements of the fault location of high-temperature superconducting cables, a fault location method of high-temperature superconducting cables based on the improved time-frequency domain reflection method and EEMD noise reduction is proposed. Considering the cross-term interference problem in the traditional time-frequency domain reflection method, this paper introduces the affine transformation to project the time-frequency distribution of the self-term and the cross term and further highlights the characteristic differences between the two through coordinate transformation, and the particle swarm algorithm is employed to solve the optimal stagger angle of the affine transformation. The unscented particle filter is adopted to separate the cross term, and EEMD noise reduction is introduced to solve the signal noise problem. Finally, two software programs, PSCAD and MATLAB, are employed for joint simulation to build a model of high-temperature superconducting cable. The simulation example shows that the proposed method in this paper can eliminate the cross-term interference of the traditional time-frequency domain reflection method, effectively locate the fault of the high-temperature superconducting cable, and improve the positioning accuracy.