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To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance. [ABSTRACT FROM AUTHOR]

Although explosive cyclones (ECs) have long been a focus of research, there remains a lack of knowledge of the statistical characteristics of their associated maximum surface winds and vertical extents. This study fills this gap by conducting a targeted statistical analysis of ECs in the Northern Hemisphere using the ERA‐I reanalysis data during a 40‐year period. Some new findings are obtained: (a) The average location of formation of ECs undergoes a notable westward and equatorward shift from September to April in the next year which is consistent with the location variations of sea surface temperature's strong gradients in subtropical regions. (b) Extreme ECs with a deepening rate more than 2.0 Bergeron or with a longer lifespan more than 10 days tend to have a larger occurrence number over the Northern Atlantic Ocean than over the Northern Pacific. (c) The maximum surface wind associated with an EC tends to appear between the EC reaching its maximum deepening rate and reaching its minimum central pressure. (d) The northeastern quadrant of ECs accounts for the highest proportion of maximum surface wind and strongest wind speed, as the baroclinic energy conversion is generally strongest in this quadrant. (e) Over 60% of ECs belong to a type of vertically deep cyclone (ECs' top levels show close relationship to ascending motions within their central regions), and they tend to reach their maximum vertical extent around the time when they reach their minimum central pressures. (f) The highest top levels of ECs exhibit an overall upward extending trend as their minimum central pressure decreases or their maximum deepening rate increases. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data and artificial intelligence, the research focus of machine learning has shifted from perception domain to cognitive computing (CC) domain. How to improve the cognitive ability through big data is becoming a research hotspot of intelligence science and technology, in which recent deep learning has been expected to spark a new wave of research on cognitive computing. This paper summarizes the research progress of cognitive computing based on deep learning in recent years. And, comparison and analysis of recent progress in deep learning and cognitive computing are presented from three aspects, that is, deep learning data representation, cognitive models, parallel computing and its applications in the big data environment. Finally, some challenges and development trends of cognitive computing based on deep learning for big data are investigated to for cast the future research. [ABSTRACT FROM AUTHOR]