首页|Studies from Xiamen University Provide New Data on Machine Learning (Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine L earning)
Studies from Xiamen University Provide New Data on Machine Learning (Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine L earning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence.According to news originating from Xiamen,People's Republic o f China,by NewsRx correspondents,research stated,"Ocean sound speed is import ant for underwater acoustic applications,such as communications,navigation and localization,where the assumption of uniformly distributed sound speed profile s (SSPs) is generally used and greatly degrades the performance of underwater ac oustic systems." Funders for this research include Department of Natural Resources of Guangdong P rovince; Recruiting Talents of Nanjing University of Posts And Telecommunication s; National Natural Science Foundation of China; Natural Resources Science And T echnology Innovation Project of Fujian Province.Our news journalists obtained a quote from the research from Xiamen University:"The acquisition of SSPs is necessary for the corrections of the sound ray propa gation paths.However,the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high cos ts of calculations and hardware deployments.This paper proposes a novel sound s peed inversion method based on multi-source ocean remote sensing observations and machine learning,which adapts to large-scale sea regions.Firstly,the datase ts of SSPs are generated utilizing the Argo thermohaline profiles and the empiri cal formulas of the sound speed.Then,the SSPs are analyzed utilizing the empir ical orthogonal functions (EOFs) to reduce the dimensions of the feature space a s well as the computational load.Considering the nonlinear regression relations of SSPs and the observed datasets,a general framework for sound speed inversio n is formulated,which combines the designed machine learning models with the re duced-dimensional feature representations,multi-source ocean remote sensing obs ervations and water temperature data."
Xiamen UniversityXiamenPeople's Repu blic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRemote Se nsing
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