MRI: Acquisition of a GPU-based High Performance Computing Instrumentation for Smart City Research at Cleveland State University
MRI:克利夫兰州立大学为智能城市研究采购基于 GPU 的高性能计算仪器
基本信息
- 批准号:2215388
- 负责人:
- 金额:$ 43.44万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-10-01 至 2025-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This project will establish a Graphics Processing Unit (GPU)-based High Performance Computing Instrumentation (HPCI) at Cleveland State University (CSU) to facilitate and promote Smart City research. The research goal is to transform Cleveland to a modern smart city with shorter commute time even during rush hours, ultra-low crime rate, a highly robust and secure electric power grid, and successful professional sports teams contending for championships. The requested major research instrumentation will enable research in the following five areas: 1) Smart Traffic; 2) Smart Vehicle; 3) Smart Internet of Things (IoT); 4) Smart Microgrid; and 5) Smart Sports. The Smart Traffic project will develop novel deep learning methods to extract useful information from remote sensing traffic images under complex urban environments. The Smart Vehicle project will improve the state-of-the-art in reliable communications and in data processing among 5G-enabled Connected Vehicles and the Vehicle-to-Everything. The Smart IoT project will develop novel machine learning methods for wireless data-driven communication and wireless intelligent sensing. The Smart Microgrid project will develop novel hybrid learning methods to ensure system resiliency, cost effectiveness, efficiency, and security of microgrid. The Smart Sports project will develop new computer vision methods to recognize and evaluate fine-grained player activities towards more efficient training and player evaluation. The research has the potential to transform Cleveland into an exemplary smart city, and the research outcome could be applicable to many other urban areas in the US. The requested HPCI will make a substantial improvement to the CSU high performance computing capabilities, which will positively attract potential collaborative research in the Cleveland Metropolitan Area and greatly improve the quality of research training at CSU. The requested HPCI will support the undergraduate/graduate student research, education and training in several computer science/engineering and civil engineering courses at CSU. The research supports the K-12 education of Cleveland Metropolitan Area and also supports the Hispanic-minority research and education. The website for the project is at https://engineering.csuohio.edu/mri-hpc/ The project website is maintained by the Computer Systems Specialist of the Washkewicz College of Engineering at Cleveland State University. The research outcome, user guidance, news, and project related information will be provided and updated in this website over the expected lifetime of the requested instrument.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.
该项目将在克利夫兰州立大学(CSU)建立一个基于图形处理单元(GPU)的高性能计算仪器(HPCI),以促进和促进智慧城市的研究。研究目标是将克利夫兰转变为一个现代化的智能城市,即使在高峰时段也要缩短通勤时间,超低犯罪率,高度健全和安全的电网,以及成功的职业运动队争夺冠军。所要求的主要研究仪器将能够在以下五个领域进行研究:1)智能交通;2)智能汽车;3)智能物联网(IoT);4)智能微电网;5) Smart Sports。智能交通项目将开发新的深度学习方法,从复杂城市环境下的遥感交通图像中提取有用信息。智能汽车项目将在支持5g的互联汽车和车到一切之间提高可靠通信和数据处理的最新水平。智能物联网项目将为无线数据驱动通信和无线智能传感开发新颖的机器学习方法。智能微电网项目将开发新的混合学习方法,以确保微电网的系统弹性、成本效益、效率和安全性。智能体育项目将开发新的计算机视觉方法来识别和评估细粒度的运动员活动,以实现更有效的训练和运动员评估。该研究有可能将克利夫兰转变为一个示范性的智慧城市,研究成果可以适用于美国许多其他城市地区。所要求的HPCI将大大提高CSU的高性能计算能力,这将积极吸引克利夫兰大都会区潜在的合作研究,并大大提高CSU的研究培训质量。申请的HPCI将支持科罗拉多州立大学计算机科学/工程和土木工程课程的本科生/研究生的研究、教育和培训。这项研究支持了克利夫兰大都会地区的K-12教育,也支持了西班牙裔少数民族的研究和教育。该项目的网站是https://engineering.csuohio.edu/mri-hpc/,该网站由克利夫兰州立大学Washkewicz工程学院的计算机系统专家维护。研究成果、用户指南、新闻和项目相关信息将在该仪器的预期使用寿命期间在本网站提供和更新。该奖项反映了美国国家科学基金会的法定使命,并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,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Hongkai Yu其他文献
Removal of an embedded duodenal foreign body in a Schizophrenic adolescent with a guidewire-formed lasso
- DOI:
10.1186/s12887-024-05241-9 - 发表时间:
2024-11-29 - 期刊:
- 影响因子:2.000
- 作者:
Yingqi Yang;Hongkai Yu;Pengpeng Cai;Chengyan Xiang;Xiaomeng Jiang - 通讯作者:
Xiaomeng Jiang
A New Method and Benchmark for Detecting Co-Saliency within a Single Image
检测单个图像内的共同显着性的新方法和基准
- DOI:
10.1109/tmm.2020.2972165 - 发表时间:
2020 - 期刊:
- 影响因子:7.3
- 作者:
Hongkai Yu;Kang Zheng;Jianwu Fang;Hao Guo;Song Wang - 通讯作者:
Song Wang
Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios
通过驾驶场景中的自监督一致性学习进行交通事故检测
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Jianwu Fang;Jiahuan Qiao;Jie Bai;Hongkai Yu;Jianru Xue - 通讯作者:
Jianru Xue
Deep Domain Adaptation Based Multi-spectral Salient Object Detection
基于深域适应的多光谱显着目标检测
- DOI:
10.1109/tmm.2020.3046868 - 发表时间:
2020 - 期刊:
- 影响因子:7.3
- 作者:
Shaoyue Song;ZHENJIANG MIAO;Hongkai Yu - 通讯作者:
Hongkai Yu
BEVFix: Deep feature enhancement for robust 3D object detection
BEVFix:用于稳健3D目标检测的深度特征增强
- DOI:
10.1016/j.neunet.2025.107675 - 发表时间:
2025-10-01 - 期刊:
- 影响因子:6.300
- 作者:
Wenxuan Li;Jian Zhou;Chi Chen;Hongkai Yu;Bo Du;Qin Zou - 通讯作者:
Qin Zou
Hongkai Yu的其他文献
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