Abstract The paper presents an approach to improve the product quality from batch to batch by exploiting the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs. The data-based optimization methodology is based on using the linear time varying (LTV) perturbation model in an iterative learning control (ILC) framework to pr...

Abstract. Linear batch codes and codes for private information retrieval (PIR) with a query size t and a restricted size r of the reconstruction sets are studied. New bounds on the parameters of such codes are derived for small values of t or r by providing corresponding constructions. By building on the ideas of Cadambe and Mazumdar, a new bound in a recursive form is derived for batch codes a...

In recent years, deep neural networks (DNN) have been widely used in many fields. Lots of effort has put into training due to their numerous parameters a network. Some complex optimizers with hyperparameters utilized accelerate the process network and improve its generalization ability. It often is trial-and-error tune these optimizer. this paper, we analyze different roles samples on parameter...

In this paper, we study the effect of batch sizes on performance of flexible manufacturing cells, where performance is evaluated in terms of flow-related measures such as part flow times, work-in-process levels, machine utilizations, and production rates. We introduce new analytical models that account for material handling, both discrete and continuous, and differentiate between process and tr...