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A future for intelligent autonomous ocean observing systems
Journal of Marine Research ( IF 1.107 ) Pub Date : 2017-11-01 , DOI: 10.1357/002224017823524035
P.F.J. Lermusiaux , D.N. Subramani , J. Lin , C.S. Kulkarni , A. Gupta , A. Dutt , T. Lolla , P.J. Haley , W.H. Ali , C. Mirabito , S. Jana

Ocean scientists have dreamed of and recently started to realize an ocean observing revolution with autonomous observing platforms and sensors. Critical questions to be answered by such autonomous systems are where, when, and what to sample for optimal information, and how to optimally reach the sampling locations. Definitions, concepts, and progress towards answering these questions using quantitative predictions and fundamental principles are presented. Results in reachability and path planning, adaptive sampling, machine learning, and teaming machines with scientists are overviewed. The integrated use of differential equations and theory from varied disciplines is emphasized. The results provide an inference engine and knowledge base for expert autonomous observing systems. They are showcased using a set of recent at-sea campaigns and realistic simulations. Real-time experiments with identical autonomous underwater vehicles (AUVs) in the Buzzards Bay and Vineyard Sound region first show that our predicted time-optimal paths were faster than shortest distance paths. Deterministic and probabilistic reachability and path forecasts issued and validated for gliders and floats in the northern Arabian Sea are then presented. Novel Bayesian adaptive sampling for hypothesis testing and optimal learning are finally shown to forecast the observations most informative to estimate the accuracy of model formulations, the values of ecosystem parameters and dynamic fields, and the presence of Lagrangian Coherent Structures.

中文翻译:

智能自主海洋观测系统的未来

海洋科学家一直梦想并最近开始通过自主观测平台和传感器实现海洋观测革命。此类自主系统要回答的关键问题是在何处、何时、以何种方式采样以获得最佳信息,以及如何以最佳方式到达采样位置。介绍了使用定量预测和基本原则回答这些问题的定义、概念和进展。概述了可达性和路径规划、自适应采样、机器学习以及将机器与科学家合作的结果。强调综合运用不同学科的微分方程和理论。结果为专家自主观测系统提供了推理引擎和知识库。它们使用一组最近的海上活动和逼真的模拟进行展示。在 Buzzards Bay 和 Vineyard Sound 区域使用相同的自主水下航行器 (AUV) 进行的实时实验首先表明,我们预测的时间最优路径比最短距离路径更快。然后介绍为阿拉伯海北部的滑翔机和浮标发布和验证的确定性和概率可达性和路径预测。用于假设检验和最优学习的新型贝叶斯自适应采样最终被证明可以预测最有信息量的观察结果,以估计模型公式的准确性、生态系统参数和动态场的值以及拉格朗日相干结构的存在。在 Buzzards Bay 和 Vineyard Sound 区域使用相同的自主水下航行器 (AUV) 进行的实时实验首先表明,我们预测的时间最优路径比最短距离路径更快。然后介绍为阿拉伯海北部的滑翔机和浮标发布和验证的确定性和概率可达性和路径预测。用于假设检验和最优学习的新型贝叶斯自适应采样最终被证明可以预测最有信息量的观察结果,以估计模型公式的准确性、生态系统参数和动态场的值以及拉格朗日相干结构的存在。在 Buzzards Bay 和 Vineyard Sound 区域使用相同的自主水下航行器 (AUV) 进行的实时实验首先表明,我们预测的时间最优路径比最短距离路径更快。然后介绍为阿拉伯海北部的滑翔机和浮标发布和验证的确定性和概率可达性和路径预测。用于假设检验和最优学习的新型贝叶斯自适应采样最终被证明可以预测最有信息量的观察结果,以估计模型公式的准确性、生态系统参数和动态场的值以及拉格朗日相干结构的存在。然后介绍为阿拉伯海北部的滑翔机和浮标发布和验证的确定性和概率可达性和路径预测。用于假设检验和最优学习的新型贝叶斯自适应采样最终被证明可以预测最有信息量的观察结果,以估计模型公式的准确性、生态系统参数和动态场的值以及拉格朗日相干结构的存在。然后介绍为阿拉伯海北部的滑翔机和浮标发布和验证的确定性和概率可达性和路径预测。用于假设检验和最优学习的新型贝叶斯自适应采样最终被证明可以预测最有信息量的观察结果,以估计模型公式的准确性、生态系统参数和动态场的值以及拉格朗日相干结构的存在。
更新日期:2017-11-01
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