Ceramics used in the high temperature environment are inevitably subjected to sudden temperature change, which may lead to catastrophic thermal shock failure due to the intrinsic brittleness of ceramics. In this paper, an experimental platform is designed to realize the in-situ observation during the thermal shock experiments. Experimental results show that all the cracks initiate from one of the edge midpoints and propagate to another one for square specimens. Such experimental observation is consistent with the maximum tensile stress zone with the maximum temperature gradient given by the finite element method (FEM). The different crack modes resulting from different heating rates after thermal shock experiments are observed and analyzed. Comparison between different clamping methods is conducted to study the effects of boundary conditions on the thermal shock experiments. Furthermore, in order to improve the thermal shock performance of alumina ceramics, crack arrest blocks are added near the edge midpoint. The thickness, shape and arrangement of the blocks are systematically investigated to understand the mechanism of improvement of thermal shock resistance. thermal shock, ultra-high temperature ceramics, crack arrest blocks, in-situ dynamic observation

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.