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RII Track-2 FEC: Artificial Intelligence on Sustainable Energy Infrastructure Network (AI SUSTEIN) and Beyond towards Industries of the Future

RII Track-2 FEC: Artificial Intelligence on Sustainable Energy Infrastructure Network (AI SUSTEIN) and Beyond towards Industries of the Future
RII Track-2 FEC:可持续能源基础设施网络人工智能 (AI SUSTEIN) 及未来产业的未来
批准号:
2119691
负责人:
Ying Huang
金额:
$597.75万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-10-01 至 2025-09-30

项目摘要

项目成果

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中文摘要
翻译
可持续的能源供应是我国工业和经济持续增长的关键驱动力。因此,有必要使当前和未来的能源基础设施更具响应性和弹性。这项以研究基础设施改善Track-2为重点的EPSCoR协作(RII Track-2 FEC)奖由北达科他州州立大学牵头,合作者包括阿肯色大学费耶特维尔分校、内华达大学拉斯维加斯分校和Nueta Hidatsa Sahnish学院,旨在促进司法管辖区各州和国家的技术进步和经济增长,同时培养为人工智能时代做好准备的新一代劳动力。司法管辖区国家面临的独特挑战,包括人口分散、极端天气条件、能源网络和/或以农业为基础的经济中的弱势地位,促使该团队探索基于人工智能的框架,能够识别能源和相关基础设施网络中的脆弱因素,量化能源基础设施的健康状况,并为安全的能源供应提供应对灾难性故障影响的自动化弹性策略。该项目将通过探索创新的人工智能、工程、经济学和运筹学研究方法,创建针对能源中断对复杂的相互依存的基础设施网络的负面影响的整体解决方案,从而克服与脆弱的能源系统相关的关键地区和国家问题。这个由工业工程、土木工程和环境工程、计算机科学(特别是人工智能)、电气工程(特别是电力系统)、公共政策和经济领域的专家组成的跨学科团队将探索相关方法,这些方法将在人工智能可以做出贡献的广泛行业中广泛传播和实施。这一合作努力将推动人工智能成为一个未来的行业,并通过提供与人工智能相关的副学位和辅修课程,对迫切需要精通人工智能的劳动力的行业产生立竿见影的影响。该团队还将支持早期职业教师、博士后、研究生和本科生,特别是来自部落和少数族裔服务机构的美洲原住民和西班牙裔参与者。人工智能可持续能源基础设施网络(AI SUSTEIN)的智力价值目标是建立一个合作研究计划,调查人工智能作为推动关键基础设施和行业根本性变革的潜力。AI SUSTEIN将开展以下研究活动:(1)了解和量化基础设施网络中的相互依赖,并使用人工智能对工业和经济方面的风险和经济影响进行评估;(2)利用实时数据开发基于人工智能的分散能源基础设施健康监测和故障预测系统;以及(3)通过人工智能增强的维护规划、优化和决策,创建一个战略框架,以提高能源基础设施和当地行业的弹性。AI SUSTEIN更广泛的影响目标是1)作为重要的信息和资源来源;2)与所有利益相关者,特别是行业合作伙伴,最终形成人工智能和数据分析方面的研究中心;3)发展多样化的劳动力,并赋予他们必要的人工智能技能。AI SUSTEIN将从事以下劳动力发展/教育工作:(1)开发关键基础设施中人工智能和数据分析的跨学科团队授课课程,并为部落和少数族裔服务机构的学生提供研究培训;(2)指导和支持初级教师;(3)为基础设施行业创建和提供关于人工智能和大数据方法的技术援助计划,以实现实时状态监测、维护规划和可靠性问题,并与业界合作,确保他们对建立NSF产学研合作研究中心的承诺和支持;以及(4)促进和协调部落学院人工智能副学士学位课程的形成,并在参与的大学提供人工智能辅修学位。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
A sustainable energy supply is a critical driver for our nation’s continued industrial and economic growth. Therefore, it is necessary to make current and future energy infrastructures more responsive and resilient. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award, led by North Dakota State University with collaborators: University of Arkansas - Fayetteville, University of Nevada - Las Vegas, and Nueta Hidatsa Sahnish College, aims to enhance technological progress and economic growth of the jurisdiction states and the nation, while creating a new generation of workforce ready for the era of AI. The unique challenges faced by the jurisdiction states, including dispersed populations, extreme weather conditions, weak positions in energy networks and/or agriculture-based economies, motivate the team to explore an AI-based framework capable of identifying the vulnerable elements in the energy and related infrastructure networks, quantifying the health of the energy infrastructure, and providing automated resilience strategies against the impacts of catastrophic failures for a secured energy supply. This project will overcome critical regional and national issues related to vulnerable energy systems by creating holistic solutions against the negative impacts of energy disruptions on complex interdependent infrastructure networks via the exploration of innovative AI, engineering, economics, and operations research methodologies. The interdisciplinary team, consisting of experts in industrial engineering, civil and environmental engineering, computer science (especially AI), electrical engineering (especially power systems), public policy and economics, will explore the related methodologies that will be widely disseminated and implemented in a broad cross-section of industries where AI can contribute. The collaborative effort will promote AI as an industry of the future and generate immediate tangible impacts on industries in desperate need of an AI-proficient workforce by offering an AI-related associate degree and minor programs. The team will also support early career faculty, postdocs, graduate and undergraduate students, especially Native American and Hispanic participants from tribal and minority serving institutions. The intellectual merit goal of the Artificial Intelligence on Sustainable Energy Infrastructure Network (AI SUSTEIN) is to establish a collaborative research program to investigate the potential of AI as a driving force for bringing about radical changes to critical infrastructures and industries. AI SUSTEIN will conduct the following research activities: (1) understand and quantify the interdependency in infrastructure networks and perform risk and economic impact assessment on both the industrial and economic aspects using AI; (2) develop a decentralized AI-based health monitoring and failure prediction system of energy infrastructure using real-time data; and (3) create a strategic framework for improving the resilience of energy infrastructure and local industries via AI-enhanced maintenance planning, optimization, and decision-making. The broader impact goals of AI SUSTEIN are to 1) serve as a vital source of information and resources; 2) partner with all the stakeholders especially industries to ultimately form a research center in AI and data analytics, and 3) develop diverse workforce and empower them with necessary AI skills. AI SUSTEIN will engage in the following workforce development/educational efforts: (1) develop an interdisciplinary team-taught course on AI and data analytics in critical infrastructure and provide research training to students from tribal and minority serving institutions; (2) mentor and support junior faculty; (3) create and offer a technical assistance program for infrastructure industry on AI and big data approaches for real-time condition monitoring, maintenance planning, and reliability issues and partner with the industries to secure their commitment and support for establishing an NSF Industry-University Cooperative Research Center; and (4) facilitate and coordinate the formation of an associate degree program on AI at a tribal college and offer an AI minor at the participating universities.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.
期刊论文(14)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/bigdata55660.2022.10020610
发表时间: 2022-12
期刊: 2022 IEEE International Conference on Big Data (Big Data)
影响因子: --
作者: [Karuna Bhaila;Yongkai Wu;Xintao Wu]
通讯作者: Karuna Bhaila;Yongkai Wu;Xintao Wu
Performance Evaluation of Bifacial PV Systems in Distribution Networks Operation: A CVR Study Considering Smart PV Inverter Control
双面光伏系统在配电网运行中的性能评估:考虑智能光伏逆变器控制的 CVR 研究
DOI: 10.1109/naps56150.2022.10012205
发表时间: 2022
期刊: 2022 North American Power Symposium (NAPS
影响因子: --
作者: [Ahanch, Mojtaba, Rouholamini, Mahdi, McCann, Roy, Wang, Caisheng]
通讯作者: Wang, Caisheng
DOI: 10.1117/12.2665241
发表时间: 2023-06
期刊:
影响因子: --
作者: [Luyang Xu;Shuomang Shi;Xingyu Wang;Fei Yan;Ying Huang]
通讯作者: Luyang Xu;Shuomang Shi;Xingyu Wang;Fei Yan;Ying Huang
DOI: 10.1061/9780784484876.023
发表时间: 2023-06
期刊: International Conference on Transportation and Development 2023
影响因子: --
作者: [Melika Ansarinejad;Ying Huang;Aaron Qiu]
通讯作者: Melika Ansarinejad;Ying Huang;Aaron Qiu
共 14 条
    CAREER: Intelligent Corrosion Mitigation System of Steel Structures with Duplex Coating
    • 批准号:
      1750316
    • 项目类别:
      Standard Grant
    • 资助金额:
      $50.0万
    • 财政年份:
      2018
    • 负责人:
      Ying Huang
    • 依托单位:
    Mathematical Sciences: Studies in Linear and Nonlinear Analysis
    • 批准号:
      9410557
    • 项目类别:
      Standard Grant
    • 资助金额:
      $1.75万
    • 财政年份:
      1994
    • 负责人:
      Ying Huang
    • 依托单位:
    海外基金