US Ignite: Collaborative Research: Focus Area 1: Fiber Network for Mapping, Monitoring and Managing Underground Urban Infrastructure
US Ignite: Collaborative Research: Focus Area 1: Fiber Network for Mapping, Monitoring and Managing Underground Urban Infrastructure
批准号:
1647175
负责人:
Dalei Wu
金额:
$29.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-01-01 至 2021-12-31
中文摘要
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英文摘要
Underground infrastructure supports services critical to daily human life, including fresh water supply, waste and storm water sewage, natural gas, electric power, steam and telecommunications. Much of the infrastructure is aging, and in unknown locations and condition. This project proposes innovative research to monitor and map underground infrastructure by integrating gigabit network-enabled sensing and mapping, cutting-edge networking, and data analytics. These real-time and automated sensing and mapping techniques can improve maintenance and management operations through better planning, incident response and condition assessment. Success will improve construction and repair efficiency, and reduce unplanned service interruptions, and accidents that harm the public safety and environment. This is a collaborative research project between the University of Vermont and the University of Tennessee at Chattanooga. The cities of Burlington, VT, Winooski, VT and Chattanooga, TN are providing access to their infrastructure facilities for testing. A notable feature is that all three cities have deployments of gigabit networks that are available for use on this project. The three participating cities are located in small metropolitan regions that ease implementation of research efforts, yet are big enough to identify key issues affecting scaling up to larger cities. This project also provides educational experiences for graduate students and participating municipal utility officials in gigabit network-enabled sensing and underground urban infrastructure. This project uses gigabit networks to integrate a mobile ground penetrating radar sensing with a cohesive network of sensors for detecting, assessing and reporting incipient conditions, such as emergent leaks. The processing and presentation of underground information will be rapidly transmitted to interested parties through secure, timely, and reliable communication, This project helps to build network-augmented position registration in an urban environment. Performance of the gigabit network-enabled utility mapping and sensing system will be measured in three participating cities. Success with this research will enable cities to manage, maintain and grow their infrastructure in manners that improve service, sustainability and resilience, while reducing costs, energy consumption and wasted resources. Since many of the aging underground infrastructure lies in older cities, often subjected to economic distress and decay, this project can help to provide basic human needs and rights, and help to provide social justice through reliable low-cost provision of clean drinking water, functional storm and waste water sewers, heat, electricity and telecommunications. Additionally, there is significant potential for increased resilience and rapid effective management of recovery from disasters.
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DOI:
10.1109/access.2018.2851392
发表时间:
2018-06
期刊:
IEEE Access
影响因子:
3.9
作者:
[Shuaishuai Guo;Dalei Wu;Haixia Zhang;D. Yuan]
通讯作者:
Shuaishuai Guo;Dalei Wu;Haixia Zhang;D. Yuan
DOI:
--
发表时间:
2019
期刊:
ISSAT International Conference on Data Science and Intelligent Systems
影响因子:
--
作者:
[Will, Rice, Omwenga, Maxwell, Wu, Dalei, Liang, Yu]
通讯作者:
Liang, Yu
Extensive Huffman-tree-based Neural Network for the Imbalanced Dataset and Its Application in Accent Recognition
不平衡数据集的扩展哈夫曼树神经网络及其在口音识别中的应用
DOI:
10.1109/icaiic51459.2021.9415243
发表时间:
2021
期刊:
2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC
影响因子:
--
作者:
[Merrill, Jeremy, Liang, Yu, Wu, Dalei]
通讯作者:
Wu, Dalei
Classifying GPR Images Using Convolutional Neural Networks
使用卷积神经网络对探地雷达图像进行分类
DOI:
10.4108/eai.21-6-2018.2276629
发表时间:
2020
期刊:
MOBIMEDIA 2018
影响因子:
--
作者:
[Almaimani, Maha, Wu, Dalei, Liang, Yu, Yang, Li, Huston, Dryver, Xia, Tian]
通讯作者:
Xia, Tian
Lidar-Based Real-Time Mapping for Digital Twin Development
基于激光雷达的实时测绘数字孪生开发
DOI:
10.1109/icme51207.2021.9428337
发表时间:
2021
期刊:
Computers and Communications
影响因子:
--
作者:
[Brock, Evan, Huang, Chengxuan, Wu, Dalei, Liang, Yu]
通讯作者:
Liang, Yu
共 9 条
Making Opportunities for Computer Science and Computer Engineering Students (MOCS)
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批准号:1259873
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项目类别:Standard Grant
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资助金额:$58.5万
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财政年份:2013
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负责人:Dalei Wu
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依托单位:
海外基金