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

CPS：TTP 选项：中：机器学习启用“智能网络”以优化可持续渔业技术

• 批准号: 1837473
• 负责人: Jennifer Blain Christen
• 金额: $ 100万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837473&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Medium; Machine

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.

渔业在全球雇佣了2.6亿人，鱼类是全球约40%人口的主要动物蛋白来源。过去几十年，全世界的捕捞努力有所增加，导致人们对非目标物种的意外捕获(称为“兼捕”)感到担忧，特别是濒危物种，如海龟、鲨鱼和海洋哺乳动物。在全球范围内，海龟的兼捕问题尤其严重，因为最近的估计表明，每年有数十万只海龟被渔具捕杀，这是对海龟继续生存的已知最大威胁。该项目通过启用网络物理系统的技术来解决这一问题。这个项目建立在对鱼类行为的观察基础上，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，以确定不同物种的传感模式的特征。其目的是开发能够威慑非目标物种的渔具，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来防止濒危物种在网中捕获，而不减少渔民的目标渔获量。使用带有标准视频、红外和声纳的水下摄像机来监测物种对各种特征的行为，它建立了一个数据库，记录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。这项关于渔业CPS技术的研究将对下一代CPS启用的渔网的设计具有无价的价值。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Energy-Efficient Image Recognition System for Marine Life

• DOI: 10.1109/tcad.2020.3012745
• 发表时间: 2020-11
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: H. S. Demir;J. Christen;S. Ozev

Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks

• DOI: 10.3389/fmars.2021.785357
• 发表时间: 2021-11
• 作者: J. Reavis;H. S. Demir;B. Witherington;Michael J. Bresette;Jennifer Blain Christen;J. Senko;S. Ozev