In this study, we introduce Generative Manufacturing Systems (GMS) as a novel
approach to effectively manage and coordinate autonomous manufacturing assets,
thereby enhancing their responsiveness and flexibility to address a wide array
of production objectives and human preferences. Deviating from traditional
explicit modeling, GMS employs generative AI, including diffusion models and
ChatGPT, for implicit learning from envisioned futures, marking a shift from a
model-optimum to a training-sampling decision-making. Through the integration
of generative AI, GMS enables complex decision-making through interactive
dialogue with humans, allowing manufacturing assets to generate multiple
high-quality global decisions that can be iteratively refined based on human
feedback. Empirical findings showcase GMS's substantial improvement in system
resilience and responsiveness to uncertainties, with decision times reduced
from seconds to milliseconds. The study underscores the inherent creativity and
diversity in the generated solutions, facilitating human-centric
decision-making through seamless and continuous human-machine interactions.

我们介绍了生成制造系统（GMS）作为一种新颖的方法，通过有效管理和协调自主制造资产，从而提高其对各种生产目标和人类偏好的响应能力和灵活性。GMS 采用生成式人工智能，包括扩散模型和 ChatGPT，进行从设想未来的隐式学习，并通过与人类的交互对话实现复杂的决策制定，使制造资产能够生成多个高质量的全球决策，并根据人类反馈进行迭代优化。经验证实，GMS 显著提高了系统的韧性和对不确定性的响应能力，决策时间从秒级降低至毫秒级，突显了生成解决方案中的创造力和多样性，通过无缝、持续的人机交互促进以人为中心的决策制定。

使用扩散模型和 ChatGPT 的生成制造系统

Generative manufacturing systems using diffusion models and ChatGPT

In Visual SLAM, achieving accurate feature matching consumes a significant
amount of time, severely impacting the real-time performance of the system.
This paper proposes an accelerated method for Visual SLAM by integrating GMS
(Grid-based Motion Statistics) with RANSAC (Random Sample Consensus) for the
removal of mismatched features. The approach first utilizes the GMS algorithm
to estimate the quantity of matched pairs within the neighborhood and ranks the
matches based on their confidence. Subsequently, the Random Sample Consensus
(RANSAC) algorithm is employed to further eliminate mismatched features. To
address the time-consuming issue of randomly selecting all matched pairs, this
method transforms it into the problem of prioritizing sample selection from
high-confidence matches. This enables the iterative solution of the optimal
model. Experimental results demonstrate that the proposed method achieves a
comparable accuracy to the original GMS-RANSAC while reducing the average
runtime by 24.13% on the KITTI, TUM desk, and TUM doll datasets.

通过将 GMS（基于网格的运动统计）与 RANSAC（随机抽样一致性）结合，加速了 Visual SLAM 方法，提高了特征匹配的准确性，从而减少了系统的实时性能影响。

一种加速视觉 SLAM 的错误匹配排除方法

An Error-Matching Exclusion Method for Accelerating Visual SLAM

The majority of data-driven wireless research leans heavily on discriminative
AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI
(GenAI) pertains to generative models (GMs) capable of discerning the
underlying data distribution, patterns, and features of the input data. This
makes GenAI a crucial asset in wireless domain wherein real-world data is often
scarce, incomplete, costly to acquire, and hard to model or comprehend. With
these appealing attributes, GenAI can replace or supplement DAI methods in
various capacities. Accordingly, this combined tutorial-survey paper commences
with preliminaries of 6G and wireless intelligence by outlining candidate 6G
applications and services, presenting a taxonomy of state-of-the-art DAI
models, exemplifying prominent DAI use cases, and elucidating the multifaceted
ways through which GenAI enhances DAI. Subsequently, we present a tutorial on
GMs by spotlighting seminal examples such as generative adversarial networks,
variational autoencoders, flow-based GMs, diffusion-based GMs, generative
transformers, large language models, to name a few. Contrary to the prevailing
belief that GenAI is a nascent trend, our exhaustive review of approximately
120 technical papers demonstrates the scope of research across core wireless
research areas, including physical layer design; network optimization,
organization, and management; network traffic analytics; cross-layer network
security; and localization & positioning. Furthermore, we outline the central
role of GMs in pioneering areas of 6G network research, including
semantic/THz/near-field communications, ISAC, extremely large antenna arrays,
digital twins, AI-generated content services, mobile edge computing and edge
AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the
multifarious challenges ahead, suggesting potential strategies and promising
remedies.

通过对 6G 网络研究中的数据驱动的无线研究、生成式人工智能、生成模型、GMs 和 6G 网络研究等关键词进行分析，本文介绍了生成模型的教程和调查，并对无线研究领域的核心问题进行了回顾和分析，提出了 GMs 在 6G 网络研究的重要作用，并针对未来可能面临的挑战提供了潜在的策略和有希望的解决办法。