石墨涂层载量对碱性锌锰电池电性能的影响

使用喷涂不同量石墨的钢壳组装碱性锌锰电池,考察石墨涂层载量对电池初始期及高温储存后电性能的影响.石墨涂层载量超过0.70 mg/cm2,会造成开路电压及放电性能下降,下降幅度随石墨涂层载量的增加及高温储存时间的延长而增大.     

Self-Adaptive Control System for Additive Manufacturing Using Double Electrode Micro Plasma Arc Welding

Wire arc additive manufacturing(WAAM)has been investigated to deposit large-scale metal parts due to its high deposition efficiency and low material cost.However,in the process of automatically manufacturing the high-quality metal parts by WAAM,several problems about the heat build-up,the deposit-path optimization,and the stability of the process parameters need to be well addressed.To overcome these issues,a new WAAM method based on the double electrode micro plasma arc welding(DE-MPAW)was designed.The circuit principles of different metal-transfer models in the DE-MPAW deposition process were analyzed theoretically.The effects between the parameters,wire feed rate and torch stand-off distance,in the process of WAAM were investigated experimentally.In addition,a real-time DE-MPAW control system was developed to optimize and stabilize the deposition process by self-adaptively changing the wire feed rate and torch stand-off distance.Finally,a series of tests were performed to evaluate the con-trol system's performance.The results show that the capability against interferences in the process of WAAM has been enhanced by this self-adaptive adjustment system.Further,the deposition paths about the metal part's layer heights in WAAM are simplified.Finally,the appearance of the WAAM-deposited metal layers is also improved with the use of the control system.     

OBE-CDIO理念下“集成电路版图分析与设计”课程教学改革与探索

针对目前集成电路版图分析与设计课程存在教学方法老化、教学模式形式化等问题,本文以OBE-CDIO教育理念为指导,对”集成电路版图分析与设计”课程的课程教学模式、课程设计、项目式教学方式和课程考核方式等方面的改革进行有益的探索与实践。将工程教学认证中对学生的专业毕业要求作为制定教学目标的依据,将BB网络平台、ISO9001等现代化教学资源和质量标准融入课堂教学,采用OBE-CDIO能力教学理念指导课程设计,实现教学环节与考核环节的科学化与多元化,通过引入课程思政激发学生学习热情,树立社会主义核心价值观,全面地培养学生的综合版图分析与设计能力和素养。     

一种磁控管用扼流线圈仿真设计

采用电路仿真软件仿真滤波组件S参数曲线,观察曲线随器件参数的变化;介绍利用三维全波电磁仿真软件HFSS简化设计流程的方法,并与测试结果对比。结果表明:利用3D仿真软件在满足器件设计精度的同时可以简化线圈的设计流程。     

基于SNaPshot技术对ABO血型亚型分型的方法

ABO亚型会导致红细胞凝集反应出现正反定型不符,目前实验室针对ABO亚型的鉴定主要使用PCR-SSP,该技术存在假阳性、通量低等缺点,因此开发可准确鉴定ABO血型亚型的方法迫在眉睫.为解决该问题,作者构建了一种基于SNaPshot技术鉴定ABO血型亚型的方法,该方法通过检测ABO基因第六外显子(261、297)、第七外显子(425、467、695、700、803、871、1054)上的9个SNP位点,并根据它们的组合鉴别14种不同的ABO等位基因.作者还提取50个外周血样本的基因组DNA,运用SNaPshot技术成功地对所有样本进行分型,共检出10种等位基因,分别为A101、A102、B01、B305、BW11、O101、O102、O02、B(A)02和CisAB.经验证,该方法适用于ABO血型亚型的分型.     

Swarming Microdroplets to a Dexterous Micromanipulator

Many astonishing biological collective behaviors exist in nature, and artificial microrobotic swarms have been developed by emulating these scenarios. However, these microswarms typically have single structures and lack the adaptability that many biological swarms exhibit to thrive in complex environments. Inspired by viscoelastic fire ant aggregations and using a combination of experiment and simulation, a strategy to trigger ferrofluid droplets into forming microswarms exhibiting both liquid-like and solid-like behaviors is reported. By spatiotemporally programming an applied magnetic field, microswarms can be liquefied to implement reversible elongation with a high aspect ratio and solidified into entireties to perform overturning and bending behaviors. It is demonstrated that reconfigurability enables the microswarm to be a mobile dexterous micromanipulator, acting not only as a soft “octopus arm” to explore a confined environment and grasp a targeted object but also adaptively navigate multiple terrains, such as uneven surfaces, curved grooves, complex mazes, high steps, narrow channels, and even wide gaps. This microrobotic swarm can reconfigure both shapes and tasks based on the demands of the environment, presenting novel solutions for a variety of applications.     

巨型水轮发电机剪断销研发与质量控制

为获得设计需要的巨型水轮发电机剪断销的剪切力,得到剪切力波动受控的批量剪断销,通过拉伸试验、冲击试验、硬度试验和剪断销剪切试验等讨论了全尺寸剪断销剪切试验的可行性,分析了剪切试验时正常剪断和非正常剪断的剪断销材料性能差异,探究了剪断销的剪切力质量稳定性控制方法。结果表明:控制剪断销料坯的布氏硬度波动,可实现间接控制剪断销剪切强度的波动;通过试验总结的六步法可达到控制批量剪断销质量和剪切力波动的目的。     

基于可穿戴传感器数据的人体行为识别数据特征提取方法

随着可穿戴设备的发展与普及,基于可穿戴传感数据进行人体行为检测展现了巨大的研究价值.目前大多人类行为识别工作都是基于视频图像展开的,然而,使用计算机视觉技术进行人类行为识别存在2个挑战:一是很难使参与数据采集的人员在自然状态下采集真实状态下的运动数据,在开展数据采集之前往往需要对参与数据采集的人员进行培训并严格规范其采集动作,最终得到的数据将是背离真实生活的数据,其研究价值将大打折扣;二是数据采集过程中还涉及采集人员的隐私保护问题.为此,提出一种基于深度学习的数据特征提取方法.首先在灵活设置卷积核的基础上引入神经网络的分支结构多尺度提取原始数据的深度特征;然后将各分支得到的数据特征进行融合并作为下一个卷积层的输入.实验结果表明,与目前主流方法相比,该方法在MHEALTH,WHARF和USCHAD这3个标准数据集上的准确率和召回率都取得了更好的效果.此外,该方法还在2个较新数据集Stanford-ECM Dataset和DATAEGO上做了验证,结果表明该方法具有较好的泛化能力.