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一个先进的双相堆叠组合技术与主动学习分类器:朝着可靠的破坏预测在阿迪蒂亚tokamakamak
Priyanka Muruganandham1, Sangeetha Jayaraman1, Kumudni Tahiliani2
1Department of CSE, Srinivasa Ramanujan Centre, SASTRA University, Kumbakonam, India.
The Review of scientific instruments
|September 30, 2024
概括
准确的托卡马克干扰预测对于反应堆安全至关重要. 一种新的带有主动学习的双相堆叠技术实现了98%的准确性,使可靠的等离子体破坏预测成为可能.
科学领域:
- 核聚变工程 核聚变工程
- 等离子体物理学的物理学
- 机器学习应用 机器学习应用
背景情况:
- 托卡马克核反应堆面临突然的等离子体限制损失 (中断) 的风险.
- 干扰性排放与非干扰性排放的准确分类对于运营安全和预测性控制至关重要.
- 现有的干扰识别方法受到噪音,变化性和适应性差的限制.
研究的目的:
- 用最少的劳动力开发一个强大而准确的tokamak破坏分类器.
- 为了提高分类准确性和可靠性,预测等离子体中断.
- 为了验证机密数据集的可靠性,用于先进的中断预测.
主要方法:
- 实施一个增强的堆叠概括模型:双相堆叠技术与基于池的积极学习 (DPST-PAL).
- 训练DPST-PAL模型在Aditya数据集中的162个诊断镜头上.
- 使用在DPST-PAL分类数据上训练的深度1D卷积预测模型进行预先破坏预测.
主要成果:
- 在阿迪蒂亚数据集上,DPST-PAL模型实现了98%的准确性和0.99的F1得分,优于传统方法.
- 深度1D卷积预测模型准确地预测了7-13毫秒的干扰,准确率为93.6%.
- 预测模型在47个不同的实验镜头上没有显示过早警报或错误分类.
结论:
- DPST-PAL模型提供了一个强大的解决方案,以高精度和高效率对托卡马克中断进行分类.
- 经过验证的数据集和预测模型为等离子体中断提供了可靠的早期预警.
- 这种方法提高了托卡马克的操作安全性,并为先进的控制策略铺平了道路.
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