Beam Detection Based on Machine Learning Algorithms

TL;DR

This paper presents a machine learning approach using a CNN and support vector regression to accurately detect free electron laser beam positions on screens, achieving high prediction accuracy.

Contribution

It introduces a novel sequence of CNN feature extraction combined with support vector regression for beam detection, improving prediction accuracy.

Findings

Achieved 85.8% correct prediction on test data.

Utilized transfer learning with a VGG16-based CNN.

Combined CNN features with support vector regression for precise detection.

Abstract

The positions of free electron laser beams on screens are precisely determined by a sequence of machine learning models. Transfer training is conducted in a self-constructed convolutional neural network based on VGG16 model. Output of intermediate layers are passed as features to a support vector regression model. With this sequence, 85.8% correct prediction is achieved on test data.