arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.11500cs.ROcs.AIcs.SYeess.SY

将大型语言模型集成到 EHD 泵控制中以实现精确颜色合成

Integration of Large Language Models in Control of EHD Pumps for Precise Color Synthesis

Nagoya University(名古屋大学);Chongqing University of Technology(重庆理工大学);National University of Singapore(新加坡国立大学);Tsinghua University(清华大学);Tokyo Institute of Technology(东京工业大学)

Yanhong Peng, Ceng Zhang, Chenlong Hu, Zebing Mao

AI总结:

提出将微调 LLM 与 Arduino 控制的 EHD 泵集成,把自然语言颜色指令转为可执行代码以实现精确混色,并通过概念实验验证其工业自动化潜力。

摘要:

本文提出了一种创新方法,将大型语言模型(LLM)与 Arduino 控制的电流体动力学(Electrohydrodynamic,EHD)泵相集成,用于自动化系统中的精确颜色合成。我们提出了一个新颖框架,采用经过微调的 LLM 解释自然语言命令,并将其转换为用于 EHD 泵控制的具体操作指令。该方法旨在增强用户与复杂硬件系统的交互,使其更加直观和高效。该方法包括四个关键步骤:使用颜色规格及相应 Arduino 代码的数据集微调语言模型;开发自然语言处理接口;将用户输入翻译为可执行的 Arduino 代码;以及控制 EHD 泵以实现精确混色。基于理论假设的概念性实验结果表明,该方法在准确颜色合成、高效语言模型解释和可靠 EHD 泵运行方面具有很高潜力。本研究将 LLM 的应用扩展到基于文本的任务之外,展示了其在工业自动化和控制系统中的潜力。在强调局限性和现实世界测试必要性的同时,本研究为 AI 在物理系统控制中的应用开辟了新途径,并为 AI 驱动自动化技术的未来发展奠定了基础。

英文摘要:

This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems. We propose a novel framework that employs fine-tuned LLMs to interpret natural language commands and convert them into specific operational instructions for EHD pump control. This approach aims to enhance user interaction with complex hardware systems, making it more intuitive and efficient. The methodology involves four key steps: fine-tuning the language model with a dataset of color specifications and corresponding Arduino code, developing a natural language processing interface, translating user inputs into executable Arduino code, and controlling EHD pumps for accurate color mixing. Conceptual experiment results, based on theoretical assumptions, indicate a high potential for accurate color synthesis, efficient language model interpretation, and reliable EHD pump operation. This research extends the application of LLMs beyond text-based tasks, demonstrating their potential in industrial automation and control systems. While highlighting the limitations and the need for real-world testing, this study opens new avenues for AI applications in physical system control and sets a foundation for future advancements in AI-driven automation technologies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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