Application of Defense Technologies to the Fisheries

国防技术在渔业中的应用

• 批准号: 9503656
• 负责人: Patrick Simpson
• 金额: $ 29.98万
• 依托单位: Scientific Fishery Systems, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 1998-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9503656&HistoricalAwards=false
• 关键词: Application; Defense; Technologies; Fisheries

*** 9503656 Simpson This Small Business Innovation Research Phase II project focuses on the development of broad bandwidth sonar methods for identifying, in real time, fish populations in open ocean environments. The approach uses sonar technology, developed in defense-related contexts over many years, that hold promise for application to fishery management. Topics that will be developed in the research involve broadband acoustics, spectral decomposition of returns, and nonlinear fuzzy neural network classifiers for characterizing and distinguishing different fish populations. In Phase I of the research, a successful application of several concepts was carried out using freshwater fish populations. Phase II research will extend the work to development of an engineering prototype for broadband fish identification in a marine environment, with both commercial and scientific-research implications Fisheries are becoming an increasingly significant food source. Currently, 30% of the protein consumed by Americans comes from the sea and this percentage is growing. Over the past decade fisheries have experienced more regulation because of bycatch, overcapitalization, and marine mammal endangerment. Because of increased regulation, the scrutiny on commercial fishing and fisheries management has become intense. As a result of these changes, techniques that can automatically and non-intrusively detect, classify, and quantify fish populations are of commercial interest to all segments of the fisheries. ***

*9503656辛普森这个小型企业创新研究第二阶段项目的重点是开发宽带声纳方法，以实时识别开阔海洋环境中的鱼类种群。这种方法使用了多年来在国防相关领域开发的声纳技术，有望应用于渔业管理。这项研究将开发的主题包括宽带声学、回波的频谱分解，以及用于表征和区分不同鱼类种群的非线性模糊神经网络分类器。在研究的第一阶段，利用淡水鱼种群成功地应用了几个概念。第二阶段的研究将把工作扩展到开发海洋环境中宽带鱼类识别的工程原型，具有商业和科学研究意义，渔业正在成为日益重要的食物来源。目前，美国人消费的蛋白质中有30%来自海洋，而且这一比例还在不断增长。在过去的十年里，由于兼捕、过度资本化和海洋哺乳动物濒危，渔业经历了更多的监管。由于加强了监管，对商业捕鱼和渔业管理的审查变得更加严格。作为这些变化的结果，能够自动和非侵入性地检测、分类和量化鱼类种群的技术对渔业的所有部分都具有商业利益。***