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Dimitris Fotakis, Panagiotis Patsilinakos, Eleni Psaroudaki and Michalis Xefteris
In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian P...
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Hugo Valayer, Nathalie Bartoli, Mauricio Castaño-Aguirre, Rémi Lafage, Thierry Lefebvre, Andrés F. López-Lopera and Sylvain Mouton
In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-int...
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Zhenwen He, Xianzhen Liu and Chunfeng Zhang
Three-dimensional voxel models are widely applied in various fields such as 3D imaging, industrial design, and medical imaging. The advancement of 3D modeling techniques and measurement devices has made the generation of three-dimensional models more con...
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Dwaipayan Chakraborty and Subhashis Mallick
Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hu...
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Yao Meng, Xianku Zhang, Guoqing Zhang, Xiufeng Zhang and Yating Duan
In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RV...
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Bingbing Zhao, Xiao He, Baoju Liu, Jianbo Tang, Min Deng and Huimin Liu
Reasonable urban commercial planning must clarify the location and scope of urban commercial districts (UCDs). However, existing studies typically detect spurious UCDs owing to the bias in a single data source while ignoring the continuity and ambiguity ...
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Tayyab Manzoor, Hailong Pei, Zhongqi Sun and Zihuan Cheng
This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model b...
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Chi Xu, Chuanqi Liu, Wanchang Zhang, Zhenghao Li and Bangsheng An
Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, base...
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Alexander Yu. Morozov and Dmitry L. Reviznikov
Problems with interval uncertainties arise in many applied fields. The authors have earlier developed, tested, and proved an adaptive interpolation algorithm for solving this class of problems. The algorithm?s idea consists of constructing a piecewise po...
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Xiaoyi Fu, Lei Liang, Wenlai Ma, Hutao Cui and Yang Zhao
Designing spacecraft involves a careful equilibrium to avoid overengineering or underdesigning, which underscores the importance of employing thermal uncertainty analysis. A key part of this analysis is modeling thermal conditions, but this is often a co...
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