The processing of bathymetric data of multi-beam echo sounders is required in the hydrographic and oceanographic field. The algorithmcube allows automatic processing of data to estimate the depth of the sea floor thr...
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ISBN:
(纸本)9781510661196;9781510661202
The processing of bathymetric data of multi-beam echo sounders is required in the hydrographic and oceanographic field. The algorithmcube allows automatic processing of data to estimate the depth of the sea floor through multi-beam data. To implement the cube algorithm, it is important to set the parameters in the data processing. Depending on the complexity of the survey, the optimization cube setting is essential to ensure the quality of results of data processing. In this paper, the optimization of cube algorithm, which is based on the configuration of the algorithm parameterization, is used to optimize the parameters of cube algorithm to improve the quality of data processing on shallow water for multi-beam echo-sounder. First, the behavior of cube is described through some different parameters to a new parameterization more suited to the EM2040C sounders. Then, tests of the cube parameterization are carried out with the aim of obtaining an optimal parameter configuration. Finally, the influence of parameters is analyzed by statistics methods to explain the feasibility of using cube algorithm to parameterize data processing on shallow water. Experimental results with the shallow water dataset demonstrate the majorization and effectiveness of the cube parameters setting for situations of the survey in areas of small depths.
Based on combined uncertainty and bathymetry estimator (cube) algorithm, a dynamic linear model (DLM) including horizontal and vertical soundings data uncertainties is discussed. Kalman filtering and multiple estimati...
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ISBN:
(纸本)9781479913343
Based on combined uncertainty and bathymetry estimator (cube) algorithm, a dynamic linear model (DLM) including horizontal and vertical soundings data uncertainties is discussed. Kalman filtering and multiple estimations are utilized to calculate the grid nodes. According to the real measured data of survey area of Bohai, comparison with the manual interactive process, cube algorithm is presented to estimate the true depth of the grid points of the survey area in this paper. From the experimental results, it can be seen that the cube algorithm is efficient and has good robustness, which means the proposed algorithm is suitable for massive multibeam soundings data in real-time automatic processing.
In recent years. computer hardware technology has greatly developed especially large memory and multi-core, but algorithm efficiency is not beneficial from the development of hardware. The fundamental reason is that t...
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ISBN:
(数字)9783642214110
ISBN:
(纸本)9783642214103
In recent years. computer hardware technology has greatly developed especially large memory and multi-core, but algorithm efficiency is not beneficial from the development of hardware. The fundamental reason is that there is insufficient utilizing CPU cache, as well as the limitations of single-thread programming. In the field of data warehousing and LAP, data cube computing is an important and time-consuming operation, how to improve efficiency of data cube calculation is continuing to pursue goals. Based on the characteristics of modern CPU, we have proposed two parallel algorithms TASK_PMW and DATA_SSMW, TASK_PMW is task-based division of the parallel algorithm, each CPU core is responsible for one Cuboid;DATA_SSMW is data partition, and scanned sharing raw data, ensure load balancing. has good scalability and high efficient. Through experiments on dual-core CPU. TASK_PMW improve 1/3. DATA_SSMW 2/3 than the original algorithm.
In the time of big data, on-line analytical processing (OLAP) is an important method to process massive data. In order to realize a system with the capacity of both high storage and high computing power, Hadoop and GP...
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In the time of big data, on-line analytical processing (OLAP) is an important method to process massive data. In order to realize a system with the capacity of both high storage and high computing power, Hadoop and GPU are both applied in OLAP. In general, three cores of OLAP determines the efficiency of OLAP analysis, which are aggregation of multi-dimensional data, pre-calculation of multi-dimensional data set (cube) and connection of dimension table and fact table. For the purpose of boosting efficiency, this paper presents optimizing algorithms for each core. Beginning with aggregation on single machine, this paper firstly designs the GPU-based aggregation algorithm. Then, GPU-based cube algorithm is introduced to accelerate pre-calculation, using inverted index to shrink computation amount. Finally, with new-designed dimension table connecting algorithm and query algorithm, GPU-based OLAP analysis algorithm is presented. Along with corresponding experiments and results, each algorithm shows their ability of boosting efficiency, optimizing GPU-based OLAP analysis on Hadoop.
Multi-beam echo sounders (MBESs) are characterized by the high resolution and high density of the sounding data. The processing of MBES bathymetry data is of special interest currently in marine surveying. The Combine...
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Multi-beam echo sounders (MBESs) are characterized by the high resolution and high density of the sounding data. The processing of MBES bathymetry data is of special interest currently in marine surveying. The Combined Uncertainty and Bathymetry Estimator (cube) and surface filtering are the main MBES-processing algorithms for outliers. These algorithms involve five adjustable parameters;however, few studies have looked at parameter optimization. In this paper, a Parameter Group Optimization (PGO) method that determines the optimal parameters of cube and surface filtering based on the seafloor topographic characteristics of the survey area is presented. The method includes typical area selection, optimal grid resolution analysis, parameter group testing and batch processing, sounding and grid analysis. Raw MBES datasets from shallow- and deep-water survey areas (between 10 and 11000 m deep) are used to validate the proposed method. The results show that when the optimized parameters are used in the cube and filtering algorithm, the outliers are automatically eliminated;the processed bathymetry data is in good agreement with the bathymetry derived by a traditional manual processing method, while the processing efficiency can be improved by more than 8 times.
Linear mixed models cover a wide range of statistical methods, which have found many uses in the estimation for complex surveys. The purpose of this work is to consider methods by which linear mixed models may be used...
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Linear mixed models cover a wide range of statistical methods, which have found many uses in the estimation for complex surveys. The purpose of this work is to consider methods by which linear mixed models may be used at the design stage of a survey to incorporate available auxiliary information. This paper reviews the ideas of balanced sampling and the cube algorithm, and proposes an implementation of the latter by which penalized balanced samples can be selected. Such samples can reduce or eliminate the need for linear mixed model weight adjustments, a result demonstrated theoretically and via simulation. Horvitz-Thompson estimators for such samples will be highly efficient for any responses well approximated by a linear mixed model in the auxiliary information. In Monte Carlo experiments using nonparametric and temporal linear mixed models, the strategy of penalized balanced sampling with Horvitz-Thompson estimation dominates a variety of standard strategies.
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