基于转移学习的温度不确定性降低算法用于大规模油地面结算监测
Tao Liu1, Tao Jiang1, Gang Liu1
1College of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究引入了一种转移学习算法,通过纠正温度诱导的错误来提高地面定位 (GS) 传感器的准确性. 该方法增强了对油箱的白天监控,确保了更安全的基础设施运营.
科学领域:
- 环境监测环境监测环境监测
- 传感器技术 传感器技术
- 数据科学是数据科学.
背景情况:
- 露天传感器,就像油箱中的地面沉积 (GS) 监测器一样,由于环境因素而面临准确性问题.
- 由于不均的阳光照射造成的不均的温度场导致GS传感器读数发生重大错误.
研究的目的:
- 分析温度变化对GS监控传感器的影响.
- 建立温度变化和由此产生的测量不确定性之间的可靠映射.
- 开发一种方法来消除温度引起的传感器数据干扰.
主要方法:
- 收集温度数据以推断温度的不确定性.
- 开发了一种基于转移学习的算法,以建模温度变化和不确定性之间的复杂关系,特别是在有限的数据下.
- 使用最小平方法 (LSM) 对线性拟合模型验证了算法的性能.
主要成果:
- 与LSM模型相比,转移学习算法表现出更高的性能.
- 拟议的算法可实现高达21.9%的精度改进.
- 该算法有效地预测温度不确定性,即使在有限的数据集.
结论:
- 开发的转移学习算法提高了GS传感器在白天监测中的准确性.
- 这一改进有助于油箱设施的安全运行和更广泛的基础设施健康监测.
- 该研究解决了复杂,非线性温度不确定性关系和数据限制所带来的挑战.
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