CSNS RCS 环 BPM 电子学实现
Nuclear Electronics & Detection Technology（2014）
Abstract
中国散裂中子源（CSNS）快循环同步环（RCS）上需要束流位置检测器（BPM）来检测束流位置偏移。论文介绍该系统中读出电路的设计和实现。设计BPM读出电路的最大挑战就是接收和处理大动态范围（5．8 mV～32 V）和变化宽度（80～500 ns）的探头引出信号。文中介绍的模拟电路，采用由模拟运放和模拟开关构成可变增益放大器的结构，能够接收和处理该探头引出信号。另外，对于一个BPM系统，精确的实时束流位置检测是必须的。本设计基于FPGA，开发了计算逐束团位置信息和束流闭轨模式位置信息的实时算法。此外，设计实现了工程所需的各种功能，包括30 s逐束团位置缓存、基于VME总线的数据读出和控制、FPGA程序的在线加载等。初步测试显示，在最小信号10 mV时，逐束团位置分辨是0．9 mm，束流闭轨模式位置分辨为50μm。
MoreTranslated text
上传PDF
View via Publisher
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