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
地下基础设施支持对人类日常生活至关重要的服务,包括淡水供应、废水和暴雨污水、天然气、电力、蒸汽和电信。许多基础设施都在老化,而且位置和状况都不清楚。该项目提出了通过集成千兆网络支持的传感和映射、尖端网络和数据分析来监测和绘制地下基础设施的创新研究。这些实时和自动化的传感和测绘技术可以通过更好的规划、事件响应和状况评估来改进维护和管理操作。成功将提高施工和维修效率,减少计划外服务中断和危害公共安全和环境的事故。这是佛蒙特州大学和田纳西大学查塔努加分校的合作研究项目。佛蒙特州的伯灵顿、佛蒙特州的维努斯基和田纳西州的查塔努加正在为测试提供基础设施。一个值得注意的特点是,这三个城市都部署了千兆网络,可用于该项目。这三个参与城市位于便于实施研究工作的小城市区域,但其规模足够大,足以确定影响扩大到较大城市的关键问题。该项目还为研究生和参与的市政公用事业官员提供千兆网络支持的传感和地下城市基础设施方面的教育经验。该项目使用千兆位网络将移动探地雷达传感器与紧密相连的传感器网络相结合,以检测、评估和报告紧急泄漏等紧急情况。地下信息的处理和呈现将通过安全、及时、可靠的通信快速传递给感兴趣的各方,该项目有助于在城市环境下建立网络增强的位置登记。千兆网络公用事业测绘和传感系统的性能将在三个参与城市进行测量。这项研究的成功将使城市能够以提高服务、可持续性和弹性的方式管理、维护和发展基础设施,同时降低成本、能源消耗和浪费的资源。由于许多老化的地下基础设施位于较老的城市,往往受到经济困境和衰败的影响,该项目可以帮助满足人们的基本需求和权利,并通过以可靠的低成本提供清洁饮用水、功能齐全的雨水和废水下水道、供热、电力和电信,帮助实现社会正义。此外,在提高复原力和快速有效地管理灾后恢复方面有很大潜力。
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.
期刊论文(11)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
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)
-
批准号:1259873
-
项目类别:Standard Grant
-
资助金额:$58.5万
-
财政年份:2013
-
负责人:Dalei Wu
-
依托单位:
海外基金