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: North Dakota State University Fargo
• 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
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119691&HistoricalAwards=false
• 关键词: RII; Track; FEC; Artificial; Intelligence

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.

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反应能力和复原力。这项研究基础设施改善轨道-2重点EPSCoR合作（RII轨道-2 FEC）奖，由北达科他州州立大学与合作者领导：阿肯色州-费耶特维尔大学，内华达州-拉斯维加斯大学和Nueta Hidatsa Sahnish学院，旨在促进管辖州和国家的技术进步和经济增长，同时创造新一代劳动力为人工智能时代做好准备。管辖州面临的独特挑战，包括分散的人口，极端天气条件，能源网络和/或农业经济的薄弱地位，促使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，以及针对灾难性故障的影响提供自动化弹性策略，以确保能源供应。该项目将通过探索创新的人工智能、工程、经济学和运筹学方法，针对能源中断对复杂的相互依赖的基础设施网络的负面影响，创建整体解决方案，以克服与脆弱能源系统相关的关键区域和国家问题。由工业工程、土木和环境工程、计算机科学（特别是人工智能）、电气工程（特别是电力系统）、公共政策和经济学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。这项合作将促进人工智能作为未来的产业，并通过提供人工智能相关的副学士学位和辅修课程，对迫切需要人工智能熟练劳动力的行业产生直接的切实影响。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民族服务机构的美洲土著和西班牙裔参与者。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，以调查人工智能作为推动关键基础设施和行业发生根本性变化的驱动力的潜力。AI SUSTEIN将开展以下研究活动：（1）了解和量化基础设施网络的相互依赖性，并使用AI对工业和经济方面进行风险和经济影响评估;（2）使用实时数据开发基于AI的分散式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工业的弹性。AI SUSTEIN更广泛的影响目标是：1）作为信息和资源的重要来源; 2）与所有利益相关者，特别是行业合作，最终形成人工智能和数据分析研究中心; 3）培养多样化的劳动力，并为他们提供必要的人工智能技能。AI SUSTEIN将参与以下劳动力发展/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，并为来自部落和少数民族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并提供人工智能和大数据方法的技术援助计划，用于实时状态监测，维护计划，和可靠性问题，并与业界合作，以确保他们的承诺和支持，建立一个NSF产学合作研究中心;以及（4）促进和协调在部落学院开设人工智能副学士学位课程，并在参与的大学开设人工智能辅修课程。该奖项反映了NSF的法定使命，并被认为值得支持通过使用基金会的知识价值和更广泛的影响审查标准进行评估。

项目成果

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• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 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
• 作者: Ahanch, Mojtaba;Rouholamini, Mahdi;McCann, Roy;Wang, Caisheng
• 通讯作者: Wang, Caisheng

Investigating combined effects between dynamic mechanical loads and corrosion on epoxy coated steel using distributed fiber optic sensors

• DOI: 10.1117/12.2665241
• 发表时间: 2023-06
• 作者: Luyang Xu;Shuomang Shi;Xingyu Wang;Fei Yan;Ying Huang

Impact of Fog on Vehicular Emissions and Fuel Consumption in a Mixed Traffic Flow with Autonomous Vehicles (AVs) and Human-Driven Vehicles Using VISSIM Microsimulation Model

• DOI: 10.1061/9780784484876.023
• 发表时间: 2023-06
• 期刊: International Conference on Transportation and Development 2023
• 作者: Melika Ansarinejad;Ying Huang;Aaron Qiu

Sustainable Development for Oil and Gas Infrastructure from Risk, Reliability, and Resilience Perspectives

• DOI: 10.3390/su15064953
• 发表时间: 2023-03
• 期刊: Sustainability
• 影响因子: 3.9
• 作者: Yasir Mahmood;Tanzina Afrin;Ying Huang;Nita Yodo