In today's uncertain and competitive market, where enterprises are subjected to increasingly shortened product life-cycles and frequent volume changes, reconfigurable manufacturing systems (RMS) applications play a significant role in the manufacturing industry's success. Despite the advantages offered by RMS, achieving a high-efficiency degree constitutes a challenging task for stakeholders and decision-makers when they face the trade-off decisions inherent in these complex systems. This study addresses work tasks and resource allocations to workstations together with buffer capacity allocation in RMS. The aim is to simultaneously maximize throughput and minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach support decision-makers with discovered knowledge to further understand the RMS design. In particular, this study presents a problem-specific customized SMO combined with a novel flexible pattern mining method for optimizing RMS and conducting post-optimal analyzes. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision-support and production planning of RMS.

过去十年迅速采用了人工智能（AI），特别是深度学习网络，在医学互联网上（IOMT）生态系统。然而，最近已经表明，深度学习网络可以通过对抗性攻击来利用，这不仅使得IOMT易受数据盗窃，而且对医学诊断的操纵。现有的研究考虑将噪声添加到原始IOMT数据或模型参数中，这不仅可以降低医学推断的整体性能，而且对从梯度方法的深度泄漏的喜好是无效的。在这项工作中，我们提出了近端渐变分流学习（PSGL）方法，用于防范模型反演攻击。所提出的方法故意在客户端进行深度神经网络培训过程时攻击IOMT数据。我们建议使用近端梯度方法来恢复梯度图和决策级融合策略以提高识别性能。广泛的分析表明，PGSL不仅为模型反演攻击提供有效的防御机制，而且有助于提高公共可用数据集的识别性能。我们分别在重建和对冲攻击图像中准确地报告17.9美元\％$和36.9美元。