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

渔业在全球雇佣了2.6亿人，鱼类是世界上大约40%人口的主要动物蛋白来源。在过去的几十年里，世界范围内的捕捞活动有所增加，这导致了对非目标物种，特别是海龟、鲨鱼和海洋哺乳动物等濒危物种的意外捕获（称为“副捕获”）的担忧。在全球范围内，误捕海龟的问题尤其严重，最近的估计表明，每年有数十万只海龟被渔具杀死，这是已知的对海龟持续生存的最大威胁。该项目通过网络物理系统支持技术解决了这个问题。这个项目建立在对鱼类行为的观察之上，不同物种对光谱的反应不同，可以用来调节它们的行为。这个智能网络项目扩展了这种观察，以确定不同物种感知模式的特征。其目的是开发渔具，特别是渔网，以阻止非目标物种。该项目使用机器学习来确定有效的线索，例如光和声音，这些线索使用尽可能少的能量来防止濒临灭绝的物种被渔网捕获，而不会减少渔民的目标捕获量。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，建立了一个数据库，记录了每个物种在不同海洋环境条件下的反应。该项目计划与美国国家海洋和大气管理局（NOAA）合作开展大规模后续研究。这项针对渔业的CPS技术的研究将对下一代CPS渔网的设计具有不可估量的价值。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。

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.