论文标题
贝叶斯推断，非线性传感器对非线性传感器的动态校准
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference
论文作者
论文摘要
大多数传感器校准都取决于其响应特征的线性和稳定性，但是实用传感器是非线性的，其响应随时间漂移，限制了他们的采用选择。为了扩大传感器的领域，以在基础动力学中允许非线性和时间降落，引入了基于贝叶斯推理的非线性，非c-cusal动态校准方法，其中估计感应值作为后条件均值，鉴于传感器测量的有限长度序列，并估计了传感器测量的有限长度序列。此外，建议在新数据到达时在线调整已经学习的校准图。该方法的有效性在配备了内部光学葡萄糖传感器的活着的大鼠的连续 - 葡萄糖监控（CGM）数据上得到了验证。为了允许选择的灵活性，还以使用FDA批准的虚拟糖尿病患者模型以及说明性的CGM传感器模型生成的合成血糖水平（BGL）数据集进行了验证。
Most sensor calibrations rely on the linearity and steadiness of their response characteristics, but practical sensors are nonlinear, and their response drifts with time, restricting their choices for adoption. To broaden the realm of sensors to allow nonlinearity and time-drift in the underlying dynamics, a Bayesian inference-based nonlinear, non-causal dynamic calibration method is introduced, where the sensed value is estimated as a posterior conditional mean given a finite-length sequence of the sensor measurements and the elapsed time. Additionally, an algorithm is proposed to adjust an already learned calibration map online whenever new data arrives. The effectiveness of the proposed method is validated on continuous-glucose-monitoring (CGM) data from an alive rat equipped with an in-house optical glucose sensor. To allow flexibility in choice, the validation is also performed on a synthetic blood glucose level (BGL) dataset generated using FDA-approved virtual diabetic patient models together with an illustrative CGM sensor model.
