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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Rafiu Oyelakin, Wenyu Yang and Peter Krebs
Fitting probability distribution functions to observed data is the standard way to compute future design floods, but may not accurately reflect the projected future pattern of extreme events related to climate change. In applying the latest coupled model...
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Van Minh Nguyen, Emma Sandidge, Trupti Mahendrakar and Ryan T. White
The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-coo...
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Lin Ma, Fuheng Ma, Wenhan Cao, Benxing Lou, Xiang Luo, Qiang Li and Xiaoniao Hao
A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance...
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Charalampos Skoulikaris
Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISF...
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