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English(EN) Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

新框架在数据有限的情况下提升燃气轮机排放预测能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个信任感知的概率框架，以改进燃气轮机机队的排放预测，尤其是在标记数据稀缺的情况下。该系统结合了多个机器学习模型，并进行置信度估计和不确定性量化，为未标记涡轮机的预测生成可靠性分数。这种方法显著降低了预测误差，最高置信度预测的平均绝对误差大幅下降，表明其在更值得信赖的工业部署方面的潜力。 AI

影响 增强了工业环境中人工智能驱动的预测性维护和监控的可靠性。

排序理由 该集群包含一篇详细介绍新的机器学习框架的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架在数据有限的情况下提升燃气轮机排放预测能力

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该集群包含一篇详细介绍新的机器学习框架的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rebecca Potts, Aiden Durrant, Rick Hackney, Georgios Leontidis ·

    面向标签数据有限燃气轮机机队的信任感知预测排放监测

    arXiv:2606.06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only…

  2. arXiv cs.LG TIER_1 English(EN) · Georgios Leontidis ·

    面向标签数据有限燃气轮机机队的信任感知预测排放监测

    Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets. In this work, a trust…

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