REU Site: BIGDatA - Big Data Analytics for Cyber-physical Systems

REU 网站：BIGDatA - 网络物理系统的大数据分析

• 批准号: 1950121
• 负责人: Huiping Cao
• 金额: $ 40.46万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950121&HistoricalAwards=false
• 关键词: REU; Site; BIGDatA; Big; Data

The funding renews a Research Experience for Undergraduates (REU) Site at New Mexico State University. Undergraduate students will participate in summer research projects guided by faculty research mentors using state-of-the-art labs and facilities. Cyber-physical systems are real-world systems, such as automotive systems, medical systems, smart homes, and manufacturing systems, that have computer algorithms and software monitoring and controlling them. These systems generate large amounts data that is complex and diverse. Big data analytics are essential to effectively use, protect, and enhance most cyber-physical systems. The REU students will participate in a variety of activities including team-based research projects, training workshops, field trips, conference participation, and other professional development opportunities that will prepare them for research in the timely and important field of big data for cyber-physical systems and prepare them for future careers as computing professionals.The project is led by researchers currently exploring aspects of big data and cyber-physical systems. The research explores big data analytics for cyber-physical systems in three intertwined dimensions: systems and architectures, models and algorithms, and visualization. The research spans four application areas including unmanned systems, smart grids, wireless sensor networks, and smart homes. The goals of the project include: motivating and solidifying students’ interest in computer science and in big data analytics and security for cyber-physical systems; providing students with problem solving and research skills; equipping students with knowledge of data management and data analytics techniques; enhancing self-confidence and self-efficacy of the student participants; equipping students with professional knowledge and skills including communication, teamwork, and ethics; and broadening participation in computing and the computing workforce.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.

这笔资金更新了新墨西哥州州立大学的本科生研究经验（REU）网站。本科生将参加由教师研究导师指导的夏季研究项目，使用最先进的实验室和设施。 网络物理系统是现实世界的系统，如汽车系统、医疗系统、智能家居和制造系统，这些系统具有计算机算法和软件来监控和控制它们。这些系统生成大量复杂多样的数据。大数据分析对于有效使用、保护和增强大多数网络物理系统至关重要。REU学生将参加各种活动，包括团队研究项目，培训讲习班，实地考察，参加会议，和其他专业发展机会，这将为他们在网络物理系统的大数据的及时和重要领域的研究做好准备，并为他们未来的职业生涯做好准备计算专业人员。该项目由目前正在探索大数据和网络物理系统的研究人员领导，物理系统。 该研究在三个相互交织的维度上探索了网络物理系统的大数据分析：系统和架构，模型和算法以及可视化。 该研究涵盖四个应用领域，包括无人驾驶系统，智能电网，无线传感器网络和智能家居。该项目的目标包括：激发和巩固学生对计算机科学和大数据分析以及网络物理系统安全的兴趣;为学生提供解决问题和研究技能;为学生提供数据管理和数据分析技术的知识;增强学生参与者的自信心和自我效能;让学生具备专业知识和技能，包括沟通，团队合作和道德;该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Comprehensive Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

机器学习技术的综合研究，以预测先进空中交通的飞行能耗

• DOI: 10.2514/6.2024-0534
• 发表时间: 2024
• 期刊: AIAA SciTech Forum
• 作者: Selje, Robert A.;Gorospe, George E.;Sun, Liang
• 通讯作者: Sun, Liang

Leveraging Energy Storage Resources to Improve Combined Cycle Gas Turbines Operational Efficiency

利用储能资源提高联合循环燃气轮机的运行效率

• DOI: 10.1109/pesgm48719.2022.9917129
• 发表时间: 2022
• 期刊: IEEE Power & Energy Society General Meeting (PESGM
• 作者: Wang, Fengyu;Ebarb, Ryan;Morales, Humberto;Lavrova, Olga;Jiang, Yazhou;Sun, Liang;Wu, Zhongyu
• 通讯作者: Wu, Zhongyu

Impact of Hydrogen Powered Drones on Advanced Air Mobility

氢动力无人机对先进空中机动性的影响

• DOI: 10.2514/6.2023-2676
• 发表时间: 2023
• 期刊: AIAA SciTech Forum 2023
• 作者: Coraspe, Daniel;Diaz-Acosta, Samuel;Serrano, Emilio;Banash, Zoey;Sun, Liang
• 通讯作者: Sun, Liang

Focused Stochastic Neighbor Embedding for Better Preserving Points of Interest

• DOI: 10.1109/bdcat56447.2022.00043
• 发表时间: 2022-12
• 期刊: 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
• 作者: Rafael Baez Ramirez;Sanuj Kumar;Tuan M. V. Le;H. Cao

Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning

• DOI: 10.2514/6.2023-0507
• 发表时间: 2023-01
• 期刊: AIAA SCITECH 2023 Forum
• 作者: Amer Al-Radaideh;Robert Selje;Daniel Coraspe;Efe Camci;R. Dutta;Liang Sun;Senthilnath Jayavelu;Xiaoli Li