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CPS: Frontier: Collaborative Research: Data-Driven Cyberphysical Systems

CPS：前沿：协作研究：数据驱动的网络物理系统

基本信息

批准号：
1646522
负责人：
Ufuk Topcu
金额：
$ 77万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646522&HistoricalAwards=false
关键词：
CPS Frontier Collaborative Research Data

项目摘要

Data-driven cyber-physical systems are ubiquitous in many sectors including manufacturing, automotive, transportation, utilities and health care. This project develops the theory, methods and tools necessary to answer the central question "how can we, in a data-rich world, design and operate cyber-physical systems differently?" The resulting data-driven techniques will transform the design and operation process into one in which data and models - and human designers and operators - continuously and fluently interact. This integrated view promises capabilities beyond its parts. Explicitly integrating data will lead to more efficient decision-making and help reduce the gap from model-based design to system deployment. Furthermore, it will blend design- and run-time tasks, and help develop cyber-physical systems not only for their initial deployment but also for their lifetime. While proposed theory, methods and tools will cut across the spectrum of cyber-physical systems, the project focuses on their implications in the emerging application of additive manufacturing. Even though a substantial amount of engineering time is spent, additive manufacturing processes often fail to produce acceptable geometric, material or electro-mechanical properties. Currently, there is no mechanism for predicting and correcting these systematic, repetitive errors nor to adapt the design process to encompass the peculiarities of this manufacturing style. A data-driven cyber-physical systems perspective has the potential to overcome these challenges in additive manufacturing. The project's education plan focuses on the already much needed transformation of the undergraduate and graduate curricula to train engineers and computer scientists who will create the next-generation of cyber-physical with a data-driven mindset. The team will reach out to K-12 students and educators through a range of activities, and to undergraduate students from underrepresented groups through year-long research projects. All educational material generated by the project will be shared publicly.

数据驱动的网络物理系统在许多领域无处不在，包括制造业、汽车、运输、公用事业和医疗保健。该项目开发了必要的理论，方法和工具来回答核心问题“我们如何在数据丰富的世界中以不同的方式设计和操作网络物理系统？”“由此产生的数据驱动技术将把设计和操作过程转变为一个数据和模型-以及人类设计师和操作员-持续和流畅地交互的过程。这种集成视图承诺了超出其部分的功能。明确地集成数据将导致更有效的决策制定，并有助于减少从基于模型的设计到系统部署的差距。此外，它将融合设计和运行时的任务，并帮助开发网络物理系统，不仅为他们的初始部署，而且为他们的生命周期。虽然提出的理论，方法和工具将跨越网络物理系统的范围，但该项目的重点是它们在增材制造新兴应用中的影响。尽管花费了大量的工程时间，增材制造工艺通常无法产生可接受的几何、材料或机电性能。目前，还没有机制来预测和纠正这些系统性的重复性错误，也没有机制来调整设计过程以涵盖这种制造风格的特点。数据驱动的网络物理系统视角有可能克服增材制造中的这些挑战。该项目的教育计划侧重于对本科和研究生课程进行急需的改革，以培养工程师和计算机科学家，他们将以数据驱动的思维方式创建下一代网络物理。该团队将通过一系列活动接触K-12学生和教育工作者，并通过为期一年的研究项目接触来自代表性不足群体的本科生。该项目产生的所有教育材料将公开分享。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Program Synthesis Using Deduction-Guided Reinforcement Learning

使用演绎引导强化学习的程序综合

DOI：
10.1007/978-3-030-53291-8
发表时间：
2020
期刊：
Computer Aided Verification - 32nd International Conference
影响因子：
0
作者：
Chen, Yanju;Wang, Chenglong;Bastani, Osbert;Dillig, Isil;Feng, Yu
通讯作者：
Feng, Yu

Automated migration of hierarchical data to relational tables using programming-by-example

使用示例编程将分层数据自动迁移到关系表

DOI：
发表时间：
2018
期刊：
Proceedings of the VLDB
影响因子：
0
作者：
Navid Yaghmazadeh, Xinyu Wang
通讯作者：
Navid Yaghmazadeh, Xinyu Wang

Active Task-Inference-Guided Deep Inverse Reinforcement Learning

DOI：
10.1109/cdc42340.2020.9304190
发表时间：
2020-01
期刊：
2020 59th IEEE Conference on Decision and Control (CDC)
影响因子：
0
作者：
F. Memarian;Zhe Xu;Bo Wu;Min Wen;U. Topcu
通讯作者：
F. Memarian;Zhe Xu;Bo Wu;Min Wen;U. Topcu

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

DOI：
10.1109/icmla.2019.00171
发表时间：
2019-07
期刊：
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
影响因子：
0
作者：
Baihong Jin;Yingshui Tan;A. Nettekoven;Yuxin Chen;U. Topcu;Yisong Yue;A. Sangiovanni-Vincentelli
通讯作者：
Baihong Jin;Yingshui Tan;A. Nettekoven;Yuxin Chen;U. Topcu;Yisong Yue;A. Sangiovanni-Vincentelli

Decentralized Online Influence Maximization

DOI：
10.1109/allerton49937.2022.9929315
发表时间：
2022-09
期刊：
2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
影响因子：
0
作者：
Yigit E. Bayiz;U. Topcu
通讯作者：
Yigit E. Bayiz;U. Topcu