Yue Shihong, Fu Keyi, Liu Liping, Zhao Yuwei
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2024 May 11;24(10):3068. doi: 10.3390/s24103068.
Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.
电阻抗断层成像传感器已广泛应用于管道参数检测与估计。在正式应用之前，必须使用足够的标记数据对传感器进行校准。然而，由于实际测量环境的高度复杂性，校准后的传感器不准确，因为标记数据可能不确定、不一致、不完整甚至无效。另外，总是可以获得带有准确标签的部分数据，这些数据可以形成强制约束来纠正其他标记数据中的错误。本文提出了一种半监督模糊聚类算法，该算法中的模糊隶属度导致一组强制约束来纠正这些不准确的标签。在挖泥船上进行的实验在准确性和稳定性方面验证了所提出的算法。这种新的模糊聚类算法通常可以减少任何传感器校准过程中标记数据的误差。
