标题： 基于传感器的分拣系统的工艺参数的贝叶斯优化，使用高斯过程作为代理模型 显示英文标题

标题： Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models

摘要： 基于传感器的分选系统能够将物料流物理地分为两个部分。 分选决策是基于所使用传感器的图像数据评估，并通过执行器来实现。 根据物料流的特性、系统的尺寸设计以及所需的分选精度，必须设置各种过程参数。 然而，由于需求和物料流组成的不断变化，需要进行连续验证和重新调整。 在本文中，我们介绍了一种用于优化、反复监控和调整基于传感器的分选系统过程参数的方法。 基于贝叶斯优化，使用高斯过程回归模型作为代理模型，以在存在不确定性的情况下满足系统行为的特定要求。 该方法在同时考虑基于两种物料输出流要求的两个可能优化目标的同时，最小化了必要的实验数量。 此外，在模型计算中确定分选精度时也考虑了不确定性。 我们通过三个示例过程参数对该方法进行了评估。 显示英文摘要

摘要： Sensor-based sorting systems enable the physical separation of a material stream into two fractions. The sorting decision is based on the image data evaluation of the sensors used and is carried out using actuators. Various process parameters must be set depending on the properties of the material stream, the dimensioning of the system, and the required sorting accuracy. However, continuous verification and re-adjustment are necessary due to changing requirements and material stream compositions. In this paper, we introduce an approach for optimizing, recurrently monitoring and adjusting the process parameters of a sensor-based sorting system. Based on Bayesian Optimization, Gaussian process regression models are used as surrogate models to achieve specific requirements for system behavior with the uncertainties contained therein. This method minimizes the number of necessary experiments while simultaneously considering two possible optimization targets based on the requirements for both material output streams. In addition, uncertainties are considered during determining sorting accuracies in the model calculation. We evaluated the method with three example process parameters.