State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200245, China.
State Key Laboratory of Ultra-Precision Machining Technology, The Hong Kong Polytechnic University, Hong Kong, China.
Sensors (Basel). 2018 Nov 21;18(11):4069. doi: 10.3390/s18114069.
This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.
本文提出了一种基于高斯过程的贝叶斯推理系统，用于实现多传感器仪器上的智能表面测量。该系统将表面测量视为时间序列数据采集过程，使用高斯过程作为数学基础，建立一个推理似然模型，通过多特征分类和多数据集回归来辅助测量过程。多特征分类提取并分类测量表面在不同尺度下的几何特征，以设计合适的复合协方差核和相应的初始采样策略。多数据集回归将设计的协方差核作为输入，使用高斯过程模型融合多传感器测量数据集，进一步通过将融合模型的可信度作为关键采样标准，自适应地细化初始采样策略。因此，可以通过连续的学习过程实现智能采样，并进行全贝叶斯处理。高斯过程模型的统计性质结合各种强大的协方差核函数，为系统提供了处理各种复杂表面的极大灵活性。
