制造数据和机器学习平台：通过IoT实现科学实验的实时的监控
The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT
摘要：
物联网设备和传感器网络为测量、监控和指导科学实验提供了新的机会。传感器、相机和仪器可以结合起来，提供以前无法实现的对正在进行的实验状态的洞察。然而，物联网设备在其生成的数据的类型、数量和速度方面可能存在很大差异，这使得充分实现这一潜力具有挑战性。事实上，近实时的地协同不同的物联网数据流可能需要使用机器学习（ML）。此外，还需要新的工具和技术来促进传感器数据的收集、聚合和操作，以简化ML模型的应用，从而充分实现物联网设备在实验室中的实用性。在这里，我们将演示如何使用Argonne开发的制造数据和机器学习（MDML）平台在制造实验中分析和使用物联网设备。MDML旨在通过提供将AI集成到网络物理系统中进行原位分析的基础设施，标准化高级数据分析和AI支持的自动化流程优化的研究和运营环境。我们将展示MDML能够处理不同的物联网数据流，使用多种计算资源，并集成ML模型来指导实验。
英文摘要：
IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.
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。
