Rupture history of the 2016 Mw 7.0 Kumamoto earthquake is constrained by using the waveforms of strong motion observations, teleseismic broadband body waves, and long‐period surface waves. Its fault geometry is modeled with Hinagu (orienting 205° and dipping 73°) and Futagawa (orienting 235° and dipping 60°), two segments. The result reconciles the difference between moment tensor solutions and the surface fault trace. It reveals a complex rupture process that initiated on the Hinagu segment in dextral motion, propagated northeastward unilaterally, and after 15 s ceased near Aso volcano with normal fault motion. The average slip, rise time, and slip rate are 1.8 m, 2.0 s, and 1.2 m/s, respectively. The rupture broke through an ~30° fault intersection without notable delay, which can be a result of dynamic “unclamping.” The northeast boundary of the largest asperity might mark the bottom of the seismogenic zone, which becomes shallower gradually near Aso volcano.

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

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