中能X光机触发系统
High Power Laser and Particle Beams（2022）
中国工程物理研究院
Abstract
介绍了中能X光机装置触发系统研制和相关实验结果,触发系统包括主机6个支路激光开关的触发和主机放电的触发.其中6个支路的触发由6台YAG四倍频激光器完成,主机放电电触发系统由1台YAG四倍频激光器来触发.实验结果表明:每台激光器出光时间抖动σ小于等于0.3 ns,激光开关导通延迟时间约25 ns,抖动σ小于等于1.2 ns,电触发系统中激光与触发器输出电压之间的时间抖动σ为0.5 ns,匹配负载上电压大于120 kV,前沿约28 ns,脉宽150 ns.中能X光机在杆箍缩二极管负载上获得最大输出为4.2 MV/100 kA的电脉冲,电压脉冲半高宽约55 ns,输出的X射线时间抖动σ为3.4 ns.实验结果表明触发系统具备对6个支路精确调节和控制的能力,确保了中能X光机装置的高可靠性.
More求助PDF
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