New Data Science for Human Operational Analysis in Smart Manufacturing

该奖项将通过推进数据科学方法，利用现场传感器的数据改进自动化机器和人类工人相结合的制造系统，为国家繁荣和经济福利做出贡献。这将使人为操作产生的操作不确定性得以量化，并纳入绩效评估和操作规划模型。该奖项还将通过为K-12，本科生和研究生提供多学科研究，培训和国际合作机会来培养下一代科学家。我们的团队将广泛传播他们的研究成果，并将数据和由此产生的软件包分享给数据科学和运营工程社区。该项目将在数据科学和智能制造领域取得重大科学进步，超越目前专注于人类动作识别的方法，将对人类动作的上下文理解分析纳入运营分析。包裹高斯分布的使用将引入与人类操作相关的新数学和概率空间，以及相关推理问题的新计算方法。这些方法将通过支持制造系统与人类操作数据的数字孪生来补充当前的智能制造研究，为科学和工程知识体系做出贡献，并提高工业生产力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to national prosperity and economic welfare by advancing data science methods for improving manufacturing systems combining automated machines and human workers using data from in-situ sensors. This will allow the operational uncertainties arising from human operations to be quantified and integrated into models for performance evaluation and operations planning. The award will also prepare the next generation of scientists by providing multidisciplinary research, training, and international collaboration opportunities for K-12, undergraduate, and graduate students. Our team will broadly disseminate their research findings and share data and the resulting software packages to the data science and operations engineering community.This project will make significant scientific advances in data science and smart manufacturing, going beyond current methods focused on human action recognition by incorporating a contextual understanding of human motions analysis in operational analysis. The use of wrapped Gaussian distributions will introduce new mathematical and probability spaces related to human operations and novel computational approaches to related inference problems. These methods will complement current smart manufacturing research by supporting digital twins of manufacturing systems with human operational data, contributing to the body of scientific and engineering knowledge and improved industrial productivity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.