WeChat Mini Program
Old Version Features

低功耗差分谐振式mA级DC电流传感器

Automation & Instrumentation(2020)

Cited 0|Views7
Abstract
利用磁致伸缩材料铁镓与双端固定石英音叉谐振器复合的结构,提出了一种数字频率输出的低功耗差分谐振式电流传感器.磁致伸缩材料作为一次敏感单元,在载流导线产生的磁场作用下产生磁致伸缩应力,石英音叉谐振器作为二次敏感单元,实现力-频率转换.采用螺旋线圈进行电流-磁场转换,2个磁敏感单元的差分传感结构在实现力-频率转换系数倍增的同时,能够抑制温度漂移的影响,大大增加了传感器的灵敏度.利用异或门混频调理电路对2个敏感单元的输出取差频,制备了差分谐振式电流传感器并进行了实验,结果表明:传感电路功耗为1.12 mW,传感器的灵敏度为25.2 Hz/A且能够分辨出5mA的电流变化.
More
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined