基于状态空间法的电动机故障诊断和识别

电动机的初期故障信号一般较弱,不易辨别,而状态空间法在小信号的处理中灵敏度很高。为达到对电动机故障的诊断和识别,试图把状态空间法应用到对电动机的故障诊断中,先通过推导,从数学理论层面分析了状态空间法对于电动机故障信号的可辨别性,再使用Matlab软件搭建状态空间的辨别模型,把实验台测试的三组数据导入到模型中进行测试。测试的结果有力地证明了此方法的可行性,并能克服快速傅里叶变换(FFT)中数据采集量较大、分辨率不高的缺点。     

新型计量斗在锰粉料制块配料中的改进应用

锰粉料制块是解决铁合金冶炼精料入炉的主要途径之一,生产过程中为解决锰粉料粘附计量斗的问题,介绍了一种新型计量斗,阐述他的结构特点和工作原理,实施改进后下料流畅,降低了劳动强度,提高了生产效率。     

快速灵敏的LC-MS/MS法测定人血浆中阿莫西林并应用于两种制剂的一致性评价

目的: 建立快速灵敏的LC-MS/MS法测定人血浆中阿莫西林浓度，并用于两种阿莫西林胶囊的一致性评价。方法: 采用岛津公司LCMS-8060型LC-MS/MS仪，以MRM模式测定阿莫西林（m/z 366.00/114.00）的浓度，d4-阿莫西林作内标（m/z 370.10/114.05），离子源为ESI源。色谱柱选用Waters ACQUITY BEH C18（2.1×50 mm，1.7 μm），梯度洗脱。血浆样本加入内标，经甲醇沉淀蛋白后取上清液进样检测。结果: 所建方法经验证，其线性、准确度、精密度、最低定量限、提取回收率、特异性、基质效应、稳定性等各项指标均符合CFDA的指导原则及最新核查标准要求，并较文献报道中的方法有处理简单、灵敏度高、色谱峰形好的优点。结论: 所建方法快速、灵敏，适用于人血浆中阿莫西林浓度的检测。用于一致性评价的样本实测，两种制剂生物等效。     

矿用钻孔深度测量仪的设计

设计了一种以STM32F103微控制器为控制核心,基于24位高精度模数转换器的钻孔深度测量仪器。利用弹性波回波测距原理,通过测量出钻孔中钻杆的长度,实现钻孔深度的快速、无损、准确的测量,主要用于井下瓦斯抽放钻孔、探放水钻孔等煤矿通防工作的施工验收。仪器小巧轻便,操作简单,单人即可完成测量工作,节约下井的人力物力,提高生产效率,最长测量长度可达250米,误差不超过1米。     

浅谈客运机车走行部风险调研及对策

针对机车走行部关键部位存在的风险及日常机车运用和检修存在的问题,进行了调查分析并提出预防对策,保证了机车走行部的运行安全。     

PCB废水总氮处理达标的工艺探讨

对PCB废水总氮的处理,从源头削减、分质收集、分质预处理到末端处理提出了多种方法,着重分析了MAP+管式微滤膜处理氨氮废水、多级组合式RO处理硝态氮废水和A2O+MBR处理综合废水,提出PCB废水总氮达标的组合工艺,项目建成后达到预期效果。     

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.