Abstract
Argo floats are autonomous floats designed to measure temperature and salinity of the world oceans. Once deployed these floats goes to as deep as 2000 m and while coming up measure temperature and salinity of the underlying ocean automatically. These floats act as a substitute to the ship-based data sets and currently as many as ~3800 are active in the global oceans. These instruments being autonomous in nature, measure and transmit data seamlessly irrespective of the weather, season, and region. However, the salinity sensors on these floats are sensitive to bio-fouling and can cause degradation to the data. As these are one time deployed and data is continuously obtained they are not available for calibration unlike the instruments on the ship. In this work ANN is used to check the degradation of the sensors and correct the same so that the data can be use in scientific analysis.
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Acknowledgement
The authors wish to thank Dr. SSC Shenoi, Director, INCOIS, Dr. TVS Udaya Bhaskar (Scientist-“E”), and J. Pavan Kumar (Scientist-“B”), INCOIS, Hyderabad for their support and guidance throughout the work in this project and for preparing the manuscript. Authors also thank Mrs. K.S. Deepthi (Assistant Professor), ANITS, Professor S.C. Satapathy (Head of Department), ANITS, Visakhapatnam for their support in the College and work.
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Satyanarayana Raju, T., Udaya Bhaskar, T.V.S., Pavan Kumar, J., Deepthi, K.S. (2017). Detecting and Correcting the Degradations of Sensors on Argo Floats Using Artificial Neural Networks. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Computer Communication, Networking and Internet Security. Lecture Notes in Networks and Systems, vol 5. Springer, Singapore. https://doi.org/10.1007/978-981-10-3226-4_30
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DOI: https://doi.org/10.1007/978-981-10-3226-4_30
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