Project MARLIN (Maritime, Acoustic, Realtime, Learning, Information and Notification)
Project MARLIN (Maritime, Acoustic, Realtime, Learning, Information and Notification)
批准号:
10065619
负责人:
金额:
$90.67万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
利用英国的科学发展,我们将开发一种颠覆性的水下监听方法,这将彻底改变我们对英国领土沃茨的监测和监管方式。我们与南安普顿大学的声音和振动研究所(ISVR)合作,正在开发新的机器学习(ML)技术,可以详细监测人类,动物和环境活动在遥远的海洋在真实的时间。我们还在开发一种将海洋传感器连接到新的低地球轨道(LEO)卫星和5G的颠覆性方法,促进海上宽带物联网(IOT)。RS Aqua作为水下噪声记录技术和海洋监测系统的领先供应商享有国际声誉,ISVR在ML应用于水下噪声分析方面拥有领先的国际专业知识。该项目将开发新一代RS Aqua水下噪声记录仪,该记录仪可以在现场内部处理大型声学数据集,并通过智能手机网络应用程序向最终用户发送宽带信息。除此之外,ISVR的博士后角色将使用新的水下数据集(由RS Aqua记录)开发新的_自学习_ ML过程,该过程将在水下记录器上运行。最终目标是开发一种可以在世界任何地方停泊在水下的仪器,并快速学习识别异常的环境有害活动。它将把有关该活动的宽带信息发送到一个网络应用程序,这样利益相关者和管理者就可以真实的做出决策。这项技术有可能彻底改变海上能源、政府机构、非政府组织和国防部门监测和管理英国海洋环境的方式。
英文摘要
Using scientific developments pioneered in the UK, we will develop a disruptive method of listening underwater that will revolutionise how we monitor and police the UK's territorial waters.In partnership with the Institute of Sound and Vibration Research (ISVR) at the University of Southampton, we are developing new machine learning (ML) techniques which make possible detailed monitoring of human, animal and environmental activity in the remote ocean in real time. We are also developing a disruptive method of connecting ocean sensors to new low earth orbit (LEO) satellites and 5G, facilitating a broadband internet of things (IOT) at sea.RS Aqua has an international reputation as a leading provider of underwater noise recording technology and oceanographic monitoring systems, and ISVR has leading international expertise in the application of ML to underwater noise analysis. The project will develop of a new generation of RS Aqua underwater noise recorders which can internally process large acoustic datasets in situ and send broadband information to the end users via a smartphone web application. Alongside this, a postdoctoral role at ISVR will use new underwater datasets (recorded by RS Aqua) to develop new _self-learning_ ML processes which will run on the underwater recorders.The end goal is to develop an instrument that can be moored underwater, anywhere in the world, and quickly learn to identify environmentally harmful activities that are out of the ordinary. It will send broadband information about that activity to a web app, so stakeholders and managers can take decisions in real time. This technology has the potential to revolutionise how offshore energy, government agencies, NGOs, and the defence sector monitor and manage the UK ocean environment.
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