scholarly journals A Parallel Method for Accelerating Visualization for Vector Tiles

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Wei Hu ◽  
Lin Li ◽  
Chao Wu ◽  
Hang Zhang ◽  
Haihong Zhu
Keyword(s):  
Parallel Computing ◽  
Decomposition Method ◽  
Computing Time ◽  
Decomposition Methods ◽  
Influential Factors ◽  
Accurate Estimation ◽  
Parallel Visualization ◽  
Linear Regressions ◽  
Geographical Feature ◽  
Feature Visualization

<p><strong>Abstract.</strong> Vector tile technology is developing rapidly and has received increasing attention in recent years. Compared to the raster tile, the vector tile has shown incomparable advantages, such as flexible map styles, suitability for high-resolution screens and ease of interaction. Recent studies on vector tiles have mostly focused on improving the efficiency on the server side and have overlooked the efficiency on the client side, which would actually affect user experience. Parallel computing provides solutions to this issue. Parallel visualization for vector tiles is a typical example of embarrassing parallelism, because there is no need for communications between computing units during parallel computing. Therefore, the performance of parallel visualization for vector tiles mainly depends on how the workload is accurately estimated and evenly decomposed onto the computing units.</p><p>The estimation of workload of vector tile visualization is essentially an accurate estimation of the computing time of geographical feature visualization in the tile. This article uses the computational weight to represent the computing time of geographical feature visualization. The visualization process for geographical feature consists of three main steps: retrieving geographical feature, symbolizing geographical feature and rendering geographical feature. This article analysis the influential factors and building the computational weight functions (CWFs) of different types of geographical feature (point, linear and area) in different visualization steps. Then, by analysing the linear relationship between the influential factors and the computing time of geographical feature visualization, the coefficients of CWFs can be obtained by linear regressions. The goodness of fit of all the linear regressions are significant (<i>R</i><sup>2</sup>&amp;thinsp;&amp;gt;&amp;thinsp;0.9), which means the computing time of geographical feature visualization, can be accurately estimated by CWFs.</p><p>Once the computational weight of vector tiles is calculated, the workload decomposition is the next key issue. The traditional decomposition methods widely used in spatial domain decomposition are based on evenly divided spatial areas, such as vertical decomposition, horizontal decomposition and so on. However, the distribution of geographical features are usually uneven, the traditional decomposition methods may introduce large imbalance of workload for parallel computing and degrade the efficiency and performance. This article proposes a workload decomposition method based on the computational weight of vector tiles to improve the parallel visualization efficiency of vector tiles. Experiments show that the computational efficiency of parallel visualization of vector tiles with the proposed workload decomposition method is 18.6% higher than that with traditional decomposition methods.</p>

Nonlinear singular one dimensional thermo-elasticity coupled system and double Laplace decomposition methods

Filomat ◽  
2017 ◽  
Vol 31 (20) ◽  
pp. 6269-6280
Author(s):  
Hassan Gadain
Keyword(s):  
Laplace Transform ◽  
Decomposition Method ◽  
Adomian Decomposition Method ◽  
Coupled System ◽  
Decomposition Methods ◽  
One Dimensional ◽  
Convergence Proof ◽  
Double Laplace Transform ◽  
Adomian Decomposition

In this work, combined double Laplace transform and Adomian decomposition method is presented to solve nonlinear singular one dimensional thermo-elasticity coupled system. Moreover, the convergence proof of the double Laplace transform decomposition method applied to our problem. By using one example, our proposed method is illustrated and the obtained results are confirmed.

scholarly journals A Note on the Decomposition Methods for Support Vector Regression

2002 ◽  
Vol 14 (6) ◽  
pp. 1267-1281 ◽  
Author(s):  
Shuo-Peng Liao ◽  
Hsuan-Tien Lin ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Regression ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Support Vector ◽  
Dual Formulation ◽  
Working Set ◽  
Number Of Iterations

The dual formulation of support vector regression involves two closely related sets of variables. When the decomposition method is used, many existing approaches use pairs of indices from these two sets as the working set. Basically, they select a base set first and then expand it so all indices are pairs. This makes the implementation different from that for support vector classification. In addition, a larger optimization subproblem has to be solved in each iteration. We provide theoretical proofs and conduct experiments to show that using the base set as the working set leads to similar convergence (number of iterations). Therefore, by using a smaller working set while keeping a similar number of iterations, the program can be simpler and more efficient.

Computing Schemes for Longitudinal Train Dynamics: Sequential, Parallel and Hybrid

2015 ◽  
Vol 10 (6) ◽  
Author(s):  
Qing Wu ◽  
Colin Cole
Keyword(s):  
Parallel Computing ◽  
Personal Computer ◽  
Computational Efficiency ◽  
Computing Time ◽  
The Other ◽  
Hybrid Scheme ◽  
Sequential Scheme ◽  
Parallel Scheme ◽  
Train Dynamics

Conventionally, force elements in longitudinal train dynamics (LTD) are determined sequentially. Actually, all these force elements are independent from each other, i.e., determination of each one does not require inputs from others. This independent feature makes LTD feasible for parallel computing. A parallel scheme has been proposed and compared with the conventional sequential scheme in regard to computational efficiency. The parallel scheme is tested as not suitable for LTD; computing time of the parallel scheme is about 165% of the sequential scheme on a four-CPU personal computer (PC). A modified parallel scheme named the hybrid scheme was then proposed. The computing time of the hybrid scheme is only 70% of the sequential scheme. The other advantage of the hybrid scheme is that only two processors are required, which means the hybrid scheme can be implemented on PCs.

Phase-Lagged Boundary Condition Methods for Aeroelastic Analysis of Turbomachines: A Comparative Study

1999 ◽  
Author(s):  
R. Srivastava ◽  
Milind A. Bakhle ◽  
Theo G. Keith ◽  
G. L. Stefko
Keyword(s):  
Boundary Condition ◽  
Comparative Study ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Navier Stokes ◽  
Fourier Decomposition ◽  
Computational Costs ◽  
Aeroelastic Analysis ◽  
Study Results ◽  
Time Marching

In the present work a comparative study of phase-lagged boundary condition methods is carried out. The relative merits and advantages of time-shifted and the Fourier decomposition methods are compared. Both methods are implemented in a time marching Euler/Navier-Stokes solver and are applied to a flat plate helical fan with harmonically oscillating blades to perform the study. Results were obtained for subsonic as well as supersonic inflows. Results for subsonic inflow showed good comparisons with published results and between the two methods along with comparable computational costs. For the supersonic inflow, despite the presence of shocks at the periodic boundary results from both the methods compared well, however, Fourier decomposition method was computationally more expensive. For linear flowfield Fourier decomposition method is best suited, especially for work-station environment. The time-shifted method is better suited for CRAY category of computers where fast input-output devices are available.

Numerical Solution of Time-Fractional Klein–Gordon Equation by Using the Decomposition Methods

2016 ◽  
Vol 11 (4) ◽  
Author(s):  
Hossein Jafari
Keyword(s):  
Iterative Method ◽  
Numerical Solution ◽  
Decomposition Method ◽  
Gordon Equation ◽  
Adomian Decomposition Method ◽  
Type Equation ◽  
Decomposition Methods ◽  
Adomian Decomposition ◽  
Klein Gordon ◽  
Klein Gordon Equation

In this paper, we apply two decomposition methods, the Adomian decomposition method (ADM) and a well-established iterative method, to solve time-fractional Klein–Gordon type equation. We compare these methods and discuss the convergence of them. The obtained results reveal that these methods are very accurate and effective.

scholarly journals Information Extraction from Satellite-Based Polarimetric SAR Data Using Simulated Annealing and SIRT Methods and GPU Processing

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 72
Author(s):  
Stanisława Porzycka-Strzelczyk ◽  
Jacek Strzelczyk ◽  
Kamil Szostek ◽  
Maciej Dwornik ◽  
Andrzej Leśniak ◽  
...  
Keyword(s):  
Decomposition Method ◽  
Decomposition Methods ◽  
Time Frame ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Computational Tools ◽  
Gpu Processing ◽  
Sar Data ◽  
Radar Wave ◽  
Single Pixel

The main goal of this research was to propose a new method of polarimetric SAR data decomposition that will extract additional polarimetric information from the Synthetic Aperture Radar (SAR) images compared to other existing decomposition methods. Most of the current decomposition methods are based on scattering, covariance or coherence matrices describing the radar wave-scattering phenomenon represented in a single pixel of an SAR image. A lot of different decomposition methods have been proposed up to now, but the problem is still open since it has no unique solution. In this research, a new polarimetric decomposition method is proposed that is based on polarimetric signature matrices. Such matrices may be used to reveal hidden information about the image target. Since polarimetric signatures (size 18 × 9) are much larger than scattering (size 2 × 2), covariance (size 3 × 3 or 4 × 4) or coherence (size 3 × 3 or 4 × 4) matrices, it was essential to use appropriate computational tools to calculate the results of the proposed decomposition method within an acceptable time frame. In order to estimate the effectiveness of the presented method, the obtained results were compared with the outcomes of another method of decomposition (Arii decomposition). The conducted research showed that the proposed solution, compared with Arii decomposition, does not overestimate the volume-scattering component in built-up areas and clearly separates objects within the mixed-up areas, where both building, vegetation and surfaces occur.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PENETAPAN MARGIN MURABAHAH PEMBIAYAAN KONSUMTIF DI BANK KALTIM SYARIAH

2014 ◽  
Vol 4 (2) ◽  
pp. 151
Author(s):  
Kenda Satya
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Influential Factors ◽  
Production Costs ◽  
Coefficient Of Determination ◽  
Selling Price ◽  
Multiple Linear Regressions ◽  
Return On Assets ◽  
The Interest Rate ◽  
Linear Regressions

This research was proposed to discover the influential factors on consumptive financing murabahah (a contract of sale of goods with the agreement on selling price and profit earned between the seller and the buyer) margin in Kaltim Sharia bank. The research instrument that had been used was the multiple linear regressions, correlation coefficient, coefficient of determination, as well as the classical assumption. Based on the analysis, the results showed that 1) Variable of Financing Deposit Ratio (X1), Return on Assets (X2), Inflation (X3) and the interest rate (X4) gave significant effect on murabahah margin Bankaltim Sharia (Y) simultaneously. The initial analysis confirmed that the first hypothesis was accepted and proven accurate because the value of probability was less than (<)0.05 namely 0.000; 2) Moreover, the next investigation found that inflation (X3) was the most dominant variable in this study for its Inflation beta value was more than (>)FDR beta value (X1), ROA (X2), and interest rates (X4) which means that the second hypothesis was rejected due to higher inflation would extensively increase production costs and prices of goods / services. Consequently, the purchasing power will decline and subsequently murabahah financing demand would automatically decreasing as well that ultimately results in reduced margins of murabahah.

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