scholarly journals Morphological Precision Assessment of Reconstructed Surface Models for a Coral Atoll Lagoon

2018 ◽  
Vol 10 (8) ◽  
pp. 2749
Author(s):  
Qi Wang ◽  
Fenzhen Su ◽  
Yu Zhang ◽  
Huiping Jiang ◽  
Fei Cheng
Keyword(s):  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Effective Means ◽  
Slope Aspect ◽  
Change Rate ◽  
Local Slope ◽  
Evaluation Approach ◽  
Multiple Precision ◽  
Surface Models ◽  
Reconstructed Surface

In addition to remote-sensing monitoring, reconstructing morphologic surface models through interpolation is an effective means to reflect the geomorphological evolution, especially for the lagoons of coral atolls, which are underwater. However, which interpolation method is optimal for lagoon geomorphological reconstruction and how to assess the morphological precision have been unclear. To address the aforementioned problems, this study proposed a morphological precision index system including the root mean square error (RMSE) of the elevation, the change rate of the local slope shape (CRLSS), and the change rate of the local slope aspect (CRLSA), and introduced the spatial appraisal and valuation approach of environment and ecosystems (SAVEE). In detail, ordinary kriging (OK), inverse distance weighting (IDW), radial basis function (RBF), and local polynomial interpolation (LPI) were used to reconstruct the lagoon surface models of a typical coral atoll in South China Sea and the morphological precision of them were assessed, respectively. The results are as follows: (i) OK, IDW, and RBF exhibit the best performance in terms of RMSE (0.3584 m), CRLSS (51.43%), and CRLSA (43.29%), respectively, while with insufficiently robust when considering all three aspects; (ii) IDW, LPI, and RBF are suitable for lagoon slopes, lagoon bottoms, and patch reefs, respectively; (iii) The geomorphic decomposition scale is an important factor that affects the precision of geomorphologic reconstructions; and, (iv) This system and evaluation approach can more comprehensively consider the differences in multiple precision indices.

An Interpolation Method for Data Containing Distortions and its Application to Aeromagnetic Data Preprocessing

2014 ◽  
Vol 644-650 ◽  
pp. 1543-1546
Author(s):  
Sheng Zhang ◽  
Xiao Hong Meng ◽  
Ya Ning Liu
Keyword(s):  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Research Field ◽  
Aeromagnetic Data ◽  
Recognition Method ◽  
Raw Data ◽  
Change Rate ◽  
Continuous Sampling ◽  
Valid Data ◽  
Data Points

Nowadays, continuous sampling data have been widely used in the research field. However, many unpredictable distortion data points, caused by varying reasons, appear in the raw data randomly. Therefore, removing distortion data points are obligatory for raw data processing. The conventional method is the artificial recognition method, which has serious problems when applied to large volumes of data. Another way is the filtering method, which is limited by application conditions, has a bad influence on valid data what people do not expect. In this paper, we proposed an effective interpolation method to remove the distortion point. This method based on the assumption that changes between adjacent points in continuous sampling data are limited. The distortions can be recognized from the magnitude and the change rate and removed. At last, the polynomial interpolation method is used to obtain the final result. Such method has been used in the preprocessing of aeromagnetic data and gets a good result.

Displacement Back Analysis for Rock Parameters of Deep Foundation Pit

2011 ◽  
Vol 94-96 ◽  
pp. 230-234
Author(s):  
Zhao Hua Jiang ◽  
Yong Xing Zhang
Keyword(s):  
Rock Mass ◽  
Elastic Modulus ◽  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Effective Means ◽  
Quadratic Function ◽  
Environmental Condition ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Rock Parameters

Numerical analysis is effective means to evaluate deep excavation influence in the sensitive environmental condition, and it is key issues to choose the elastic modulus of rock parameters reasonably. Based on the finite element theory, the relation between rock mass elastic modulus reciprocaland and the objective function value is a quadratic function in the elastic zone of multilayer layers, and then the optimal value of elastic modulus is obtained by polynomial interpolation method. Combined with the practice engineering, the elastic modulus of rock mass excavation is back calculated in the sensitive environmental condition and the effects of excavation are evaluated on the surrounding tunnel. The results show that the method is more convenient, and provide a reference for foundation pit construction.

scholarly journals Comparative study of the implementation of the Lagrange interpolation algorithm on GPU and CPU using CUDA to compute the density of a material at different temperatures

2021 ◽  
Vol 119 ◽  
pp. 07002
Author(s):  
Youness Rtal ◽  
Abdelkader Hadjoudja
Keyword(s):  
Parallel Computing ◽  
Graphics Processing Units ◽  
Lagrange Interpolation ◽  
Polynomial Interpolation ◽  
Programming Model ◽  
Interpolation Method ◽  
Processing Unit ◽  
Central Processing ◽  
Computational Performance ◽  
Different Temperatures

Graphics Processing Units (GPUs) are microprocessors attached to graphics cards, which are dedicated to the operation of displaying and manipulating graphics data. Currently, such graphics cards (GPUs) occupy all modern graphics cards. In a few years, these microprocessors have become potent tools for massively parallel computing. Such processors are practical instruments that serve in developing several fields like image processing, video and audio encoding and decoding, the resolution of a physical system with one or more unknowns. Their advantages: faster processing and consumption of less energy than the power of the central processing unit (CPU). In this paper, we will define and implement the Lagrange polynomial interpolation method on GPU and CPU to calculate the sodium density at different temperatures Ti using the NVIDIA CUDA C parallel programming model. It can increase computational performance by harnessing the power of the GPU. The objective of this study is to compare the performance of the implementation of the Lagrange interpolation method on CPU and GPU processors and to deduce the efficiency of the use of GPUs for parallel computing.

Threshold value estimation of journey-distance using generalized polynomial function

2021 ◽  
pp. 1-17
Author(s):  
Roy Subhojit
Keyword(s):  
Polynomial Function ◽  
Polynomial Interpolation ◽  
Interpolation Method ◽  
A Priori ◽  
Threshold Value ◽  
Rate Of Change ◽  
Cumulative Frequency Distribution ◽  
Generalized Polynomial ◽  
Value Estimation ◽  
Mode A

The present work demonstrates an experience in estimating the threshold value of journey distances travelled by transit passengers using generalized polynomial function. The threshold value of journey distances may be defined as that distance beyond which passengers might no more be interested to travel by their reported mode. A knowledge on this threshold value is realized to be useful to limit the upper-most slab of transit fare, while preparing of a length-based fare matrix table. Theoretically, the threshold value can be obtained at that point on the cumulative frequency distribution (CFD) curve of journey distances at which the maximum rate of change of the slope of curve occurs. In this work, the CFD curve of the journey distance values is empirically modelled using Newton’s Polynomial Interpolation method, which helps to overcome various challenges usually encountered while an assumption of a theoretical probability distribution is considered a priori for the CFD.

PNC in 2D Curve Modeling

2017 ◽  
pp. 87-131
Keyword(s):  
Curve Fitting ◽  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Shape Representation ◽  
Linear Interpolation ◽  
Distribution Functions ◽  
Novel Approach ◽  
Curve Modeling ◽  
Basic Probability ◽  
Pros And Cons

Interpolation methods and curve fitting represent so huge problem that each individual interpolation is exceptional and requires specific solutions. PNC method is such a novel tool with its all pros and cons. The user has to decide which interpolation method is the best in a single situation. The choice is yours if you have any choice. Presented method is such a new possibility for curve fitting and interpolation when specific data (for example handwritten symbol or character) starts up with no rules for polynomial interpolation. This chapter consists of two generalizations: generalization of previous MHR method with various nodes combinations and generalization of linear interpolation with different (no basic) probability distribution functions and nodes combinations. This probabilistic view is novel approach a problem of modeling and interpolation. Computer vision and pattern recognition are interested in appropriate methods of shape representation and curve modeling.

scholarly journals GIS based mapping and analysis of landslide hazard’s impact on tourism: a case study of Balakot valley, Pakistan

2017 ◽  
Vol 5 (2) ◽  
pp. 116
Author(s):  
Alamgeer Hussain ◽  
Mobushir Riaz Khan ◽  
Naeem Abbas Malik ◽  
Muhammad Amin ◽  
Mazhar Hussain Shah ◽  
...  
Keyword(s):  
Slope Failure ◽  
Interpolation Method ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Mountainous Area ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Hazard Class ◽  
Zonation Map ◽  
Tourism Sector

The Landslide occurs in mountainous area due to failure of slope through intensive rain and earthquake. Region wise Himalayan is one of prone area of world in context of slope failure hazard; i.e. Landslide, especially Balakot valley is well known for damage of public infrastructure, roads and badly affected the tourism sector. The objective of this study is to develop landslide hazard map and database inventory of balakot tehsil and identify the Tourist resorts landslide hazard condition and hazard prone road site and developed guidelines for tourist about hazardous site and their intensity of landside, which could be useful for tourism sector and sustainable development in balakot valley. In this study we used weighted overlay analysis in arc GIS environment on primary and secondary data raster layers, like slope map, Slope Aspect map, precipitation and seismic raster maps were used to develop landslide hazard zonation map of balakot tehsil. Slope and Aspect map were developed using 30 meter aster digital elevation model. Precipitation map were developed through Inverse Distance weighted (IDW) interpolation method on annual precipitation data acquired from Pakistan meteorological department. Seismic map were acquired from Geological Survey of Pakistan (GSP). Landslide zonation map has three hazards class high, Medium and low. The landslide exposure of high hazard class 499 sq.km while, Medium class 1016 sq.km and low hazard class having 749 sq. km exposure in balakot tehsil respectively. Landslide hazard zonation mapping using GIS and RS is the best way to assess the risk of landslide hazard in mountainous areas. The study recommended that ground penetrating radar (GPR) and soil testing based research well help to understand in-depth of landslide hazard condition in balakot valley.

APPLICATION OF THE POLYNOMIAL INTERPOLATION METHOD FOR DETERMINING PERFORMANCE CHARACTERISTICS OF A DIESEL ENGINE

2014 ◽  
Vol 21 (1) ◽  
pp. 157-168 ◽  
Author(s):  
Tomasz Stoeck ◽  
Karol Franciszek Abramek
Keyword(s):  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Sufficient Accuracy ◽  
Performance Characteristics ◽  
Combustion Engines ◽  
Lagrange Polynomial ◽  
Minimum Number ◽  
Mathematical Equations ◽  
Bench Testing ◽  
Engine Type

Abstract The article shows the methodology and calculation procedures based on Lagrange polynomial interpolation which were used to determine standard performance characteristics of the Polish production engine, type ANDORIA 4CTi90-1BE6. They allow to simplify the experimental research by maintaining a minimum number of measurement points and estimating the remaining data in an analytical way. The methods presented are convenient when it comes to the practical side because they eliminate the need for exploration of mathematical equations describing the various curves, which can be cumbersome and time consuming in the case of nonautomated accounts. The results of analysis were applied to actual experimental results, indicating sufficient accuracy of the resulting approximations. As a result, procedures may be used in bench testing of a similar profile, especially with repeated cycles of the experiment, such as optimization of operating parameters of combustion engines.

Integration of statistical and spatial methods for distributing precipitation in tropical areas

2012 ◽  
Vol 44 (6) ◽  
pp. 982-994 ◽  
Author(s):  
Mandana Abedini ◽  
Md Azlin Md Said ◽  
Fauziah Ahmad
Keyword(s):  
Multivariate Regression ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Environmental Research ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Annual Average ◽  
Rain Gauges ◽  
Kriging Interpolation Method ◽  
Topographical Factors

The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta Catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.

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