基于时延自相关降噪与局部特征尺度分解的齿轮故障诊断

In order to detect the gear fault accurately, a mode based on autocorrelation denoising and local characteristic-scale decomposition（LCD） was proposed. The autocorrelation function of the vibration signal of gear box was calculated,and the low delay time of [-20,20] and high delay time of last 20 points of the autocorrelation function were set to zeros. The delayed autocorrelation denoising signal was decomposed into some intrinsic mode components（ISC） by LCD, and the ISC included the mesh frequency were selected as the effective component. The fault was detected by the envelope aptitude spectrum. The analysis of broken tooth of gear fault data shows that the increase of the signal to noise ratio（SNR）can be 8.0963dB,the method can realize the occurrence of the fault effectively, can also detect the type of the gear fault, the method can effectively support fault diagnosis.     

可重构机器人在科普中的应用

本文介绍乐高机器人的可重构性,给出了几种乐高机器人在实际科普展品中的应用案例,重点描述乐高解魔方机器人的搭建思路、关键技术和相关的一套完整的科普展示系统,充分展现了乐高可重构机器人在科普中的重要应用价值.     

基于BERT和知识蒸馏的航空维修领域命名实体识别

针对军事航空维修领域命名实体识别训练数据少，标注成本高的问题，改进提出一种基于预训练BERT的命名实体识别方法，借鉴远程监督思想，对字符融合远程标签词边界特征得到特征融合向量，送入BERT生成动态字向量表示，连接CRF模型得到序列的全局最优结果，在自建数据集上进行实验，F1值达到0.861。为压缩模型参数，使用训练好的BERT-CRF模型生成伪标签数据，结合知识蒸馏技术指导参数量较少的学生模型BiGRU-CRF进行训练。实验结果表明，与教师模型相比，学生模型以损失2%的F1值为代价，参数量减少了95.2%,运算推理时间缩短了47%。