CPS: TTP Option: Medium: Machine learning enabled "smart nets" to optimize sustainable fisheries technologies

渔业在全球雇用了2.6亿人，鱼类是世界约40%人口的主要动物蛋白质来源。在过去几十年中，全世界的捕捞努力有所增加，导致对非目标物种的附带捕获（称为"兼捕"）的关注，特别是海龟、鲨鱼和海洋哺乳动物等濒危物种。在全球范围内，海龟的误捕问题尤其严重，因为最近的估计表明，每年有数十万只海龟在渔具中被杀死，这是对其持续生存的最大已知威胁。该项目通过网络物理系统支持的技术来解决这一问题。该项目建立在对鱼类行为的观察基础上，即物种对光谱的反应不同，可以用来调节它们的行为。这个智能网项目扩展了这种观察，以确定不同物种的感知方式的特征。其目的是开发能够阻止非目标鱼种的渔具，特别是渔网。该项目使用机器学习来确定有效的线索，例如，光和声音，使用尽可能少的功率，以防止在不减少渔民的目标渔获量的情况下捕获濒危物种。它使用带有标准视频、红外线和声纳的水下摄像机来监测物种对各种特征的行为，并建立了每个物种在不同海洋环境条件下的反应数据库。该项目计划与国家海洋和大气管理局（NOAA）合作开展大规模后续研究。这项针对CPS技术的渔业研究对于下一代CPS渔网的设计将是非常宝贵的。该奖项反映了NSF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。

Fisheries employ 260 million people globally and fish are a primary animal protein source for roughly 40% of the world's population. Fishing effort has increased worldwide over the past few decades, leading to concerns over the incidental capture (termed "bycatch") of non-target species, especially endangered species such as sea turtles, sharks, and marine mammals. Globally, bycatch of sea turtles is especially problematic as recent estimates suggest that hundreds of thousands of turtles are killed annually in fishing gear, representing the greatest known threat to their continued survival. This project addresses this problem through cyber-physical system-enabled technologies. This project builds on an observation about fish behavior that species respond differently to the light spectrum and that can be used to modulate their behaviors. This smart nets project extends that observation to determine signatures for sensing modalities of different species. The intent is to develop fishing gear, specifically fishing nets, that can deter non-target species. The project uses machine learning to determine effective cues, e.g., light and sound that uses the least amount of power possible to prevent an endangered species from capture in the nets without decreasing the fishermen's target catch. Using underwater cameras with standard video, infrared, and sonar to monitor species behavior to various signatures, it builds a database of the responses for each species under varying oceanic environment conditions. The project plans large-scale follow-up studies in partnership with the National Oceanic and Atmospheric Administration (NOAA). This research on CPS technology for the fishing industry will be invaluable to the design of the next-generation of CPS-enabled fishing nets.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.