A Study of Throughput Prediction using Convolutional Neural Network over Factory Environment
Yafei Hou, Kazuto Yano, Norisato Suga, Julian L. Webber, Eiji Nii, Toshihide Higashimori, Satoshi Denno, Yoshinori Suzuki · 2021
In this paper, using the time-series throughput data generated from a simulated factory scenario, we study throughput prediction using convolutional neural network (CNN). Different with image or numerical recognition using CNN, in which the distribution of the prediction target during training stage usually has the similar level, the distribution of the throughput data concentrates only on several values. This centralized distribution may degrade the prediction accuracy. Therefore, we will propose a new CNN prediction method employing target vectorization which can mitigate the centralization of distribution. This method makes training process of CNN hold more possibility and improves the prediction accuracy of the throughput.