MOGAN: Morphologic-structure-aware Generative Learning from a Single Image

In most interactive image generation tasks, given regions of interest (ROI) by users, the generated results are expected to have adequate diversities in appearance while maintaining correct and reasonable structures in original images. Such tasks become more challenging if only limited data is available. Recently proposed generative models complete training based on only one image. They pay much attention to the monolithic feature of the sample while ignoring the actual semantic information of different objects inside the sample. As a result, for ROI-based generation tasks, they may produce inappropriate samples with excessive randomicity and without maintaining the related objects' correct structures. To address this issue, this work introduces a MOrphologic-structure-aware Generative Adversarial Network named MOGAN that produces random samples with diverse appearances and reliable structures based on only one image. For training for ROI, we propose to utilize the data coming from the original image being augmented and bring in a novel module to transform such augmented data into knowledge containing both structures and appearances, thus enhancing the model's comprehension of the sample. To learn the rest areas other than ROI, we employ binary masks to ensure the generation isolated from ROI. Finally, we set parallel and hierarchical branches of the mentioned learning process. Compared with other single image GAN schemes, our approach focuses on internal features including the maintenance of rational structures and variation on appearance. Experiments confirm a better capacity of our model on ROI-based image generation tasks than its competitive peers

A Petri-Net-Based Scheduling Strategy for Dual-Arm Cluster Tools With Wafer Revisiting

International audienceThere are wafer fabrication processes in cluster tools that require wafer revisiting. The adoption of a swap strategy for such tools forms a 3-wafer cyclic (3-WC) period with three wafers completed in each period. It has been shown that, by such a scheduling strategy, the minimal cycle time cannot be reached for some cases. This raises a question of whether there is a scheduling method such that the performance can be improved. To answer this question, a dual-arm cluster tool with wafer revisiting is modeled by a Petri net. Based on the model, the dynamical behavior of the process is analyzed. Then, a 2-wafer cyclic (2-WC) scheduling strategy is revealed for the first time. Cycle time analysis is conducted for the proposed strategy to evaluate its performance. It shows that, for some cases, the performance obtained by a 2-WC schedule is better than that obtained by any existing 3-WC ones. Thus, they can be used to complement each other in scheduling dual-arm cluster tools with wafer revisiting. Illustrative examples are given