用贝叶斯优化自动调参，提升传感器分选系统的准确率与稳定性。
Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models
- 用高斯过程做代理模型，高效搜索最优参数组合。
- 仅需少量实验即可满足双输出流的精度要求。
- 适合需要持续调优的工业分选系统，如矿业、回收行业。
基于传感器的分选系统可将物料流分为两个组分，其分选决策依赖于传感器图像数据的评估，并通过执行器实现。分选参数需根据物料特性、系统设计及所需分选精度进行设置，但因需求和物料组成变化，需持续验证与调整。本文提出一种方法，用于反复监测并优化此类系统的工艺参数。基于贝叶斯优化，采用高斯过程回归作为代理模型，在考虑不确定性的同时，实现对系统行为的具体目标。该方法在减少实验次数的前提下，同时兼顾两个输出流的优化目标。此外，模型计算中也纳入了分选准确率的不确定性。我们以三个典型工艺参数为例验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。