Updating topographic maps in multi-representation databases is crucial to a number of applications. An efficient way to update topographic maps is to propagate the updates from large-scale maps to small-scale maps. Because objects are often portrayed differently in maps of different scales, it is a complicated process to produce multi-scale topographic maps that meet specific cartographical cri...

We are interested in the efficient and reliable numerical solution of parametric multi-scale problems, the multi-scale (parametric) character of which is indicated by ε (μ) if expressed in the general notation of (1). It is well known that solving parametric multi-scale problems accurately can be challenging and computationally costly for small scales ε and for a strong dependency of the soluti...

Multi-scale representation of spatial data is a research focus in GIS, while building multi-scale data model is a key to implementing multi-scale representation of vector data. In view of the shortcomings of existing multi-scale data model in geographical cognition and special analysis, this paper puts forward a method of feature-based, and studies on it qualitatively from definition, descripti...

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthog...

The use of deep learning for fault diagnosis is already a common approach. However, integrating discriminative information types and scales into models rich multitask feature still deserves attention. In this study, multitask-based multiscale fusion network model (MEAT) proposed to address the limitations poor adaptability traditional convolutional neural complex jobs. performed multidimensiona...

We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across scales, (2) multi-scale regression network at the end to globally optimize the ...

We introduce a dynamical low-rank method to reduce the computational complexity for solving multi-scale multi-dimensional linear transport equation. The is based on macro-micro decomposition of proposed numerical uses low rank approximation only micro part solution. time and spatial discretizations are done properly so that overall scheme second order accurate asymptotic-preserving (AP); is, in...

in this study, we modify an iterative non-optimal without memory method, in such a way that is becomes optimal. therefore, we obtain convergence order eight with the some functional evaluations. to justify our proposed method, some numerical examples are given.

In this dissertation a coupled multi-scale computational model for simulating nonlinear deformation processes in crystalline metals at finite temperatures is developed. The computational model uses the finite element method to model the coarse scale response of the material. The constitutive response in the finite element will be modeled through interatomic potentials acting on the underlying h...