scholarly journals The Reduction Method of Bathymetric Datasets that Preserves True Geodata

2019 ◽  
Vol 11 (13) ◽  
pp. 1610 ◽  
Author(s):  
Marta Wlodarczyk-Sielicka ◽  
Andrzej Stateczny ◽  
Jacek Lubczonek
Keyword(s):  
Data Reduction ◽  
Reduction Method ◽  
Large Data ◽  
Water Area ◽  
Bathymetric Data ◽  
Remote Sensors ◽  
Depth Data ◽  
Coastal Modelling ◽  
Reduction Methods ◽  
Quality And Reliability

Water areas occupy over 70 percent of the Earth’s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and the objects located on it. Information about the quality and reliability of the depth data is important, especially during coastal modelling. In-shore areas are liable to continuous transformations and they must be monitored and analyzed. Presently, bathymetric geodata are usually collected via modern hydrographic systems and comprise very large data point sequences that must then be connected using long and laborious processing sequences including reduction. As existing bathymetric data reduction methods utilize interpolated values, there is a clear requirement to search for new solutions. Considering the accuracy of bathymetric maps, a new method is presented here that allows real geodata to be maintained, specifically position and depth. This study presents a description of a developed method for reducing geodata while maintaining true survey values.

scholarly journals Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6207
Author(s):  
Marta Wlodarczyk-Sielicka ◽  
Wioleta Blaszczak-Bak
Keyword(s):  
Data Reduction ◽  
Spatial Data ◽  
Autonomous Vehicles ◽  
Numerical Models ◽  
Mesh Size ◽  
Bathymetric Data ◽  
Large Sets ◽  
Reliable Source ◽  
Reduction Methods ◽  
Data Reduction Method

Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor. One such system is the multibeam echosounder (MBES), which collects very large sets of bathymetric data. The development and analysis of such large sets are laborious and expensive. Reduction of the spatial data obtained from bathymetric and other systems collecting spatial data is currently widely used. In commercial programs used in the development of data from hydrographic systems, methods of interpolation to a specific mesh size are very frequently used. The authors of this article previously proposed original the true bathymetric data reduction method (TBDRed) and Optimum Dataset (OptD) reduction methods, which maintain the actual position and depth for each of the measured points, without their interpolation. The effectiveness of the proposed methods has already been presented in previous articles. This article proposes the fusion of original reduction methods, which is a new and innovative approach to the problem of bathymetric data reduction. The article contains a description of the methods used and the methodology of developing bathymetric data. The proposed fusion of reduction methods allows the generation of numerical models that can be a safe, reliable source of information, and a basis for design. Numerical models can also be used in comparative navigation, during the creation of electronic navigation maps and other hydrographic products.

scholarly journals Improving learning vector quantization using data reduction

2019 ◽  
Vol 5 (3) ◽  
pp. 218 ◽  
Author(s):  
Pande Nyoman Ariyuda Semadi ◽  
Reza Pulungan
Keyword(s):  
Vector Quantization ◽  
Learning Process ◽  
Data Reduction ◽  
Reduction Method ◽  
Learning Algorithm ◽  
Learning Vector Quantization ◽  
Reduction Methods ◽  
Using Data ◽  
Data Reduction Method ◽  
Bank Marketing

Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively.

A Comparison of Data-Reduction Methods for a Seven-Hole Probe

2002 ◽  
Vol 124 (2) ◽  
pp. 523-527 ◽  
Author(s):  
David Sumner
Keyword(s):  
Data Reduction ◽  
Reduction Method ◽  
Interpolation Method ◽  
Low Flow ◽  
Leeward Side ◽  
Pressure Probe ◽  
Calibration Data ◽  
Grid Spacing ◽  
Reduction Methods ◽  
Data Reduction Method

Two data-reduction methods were compared for the calibration of a seven-hole conical pressure probe in incompressible flow. The polynomial curve-fit method of Gallington and the direct-interpolation method of Zilliac were applied to the same set of calibration data, for a range of calibration grid spacings. The results showed that the choice of data-reduction method and the choice of calibration grid spacing each have an influence on the measurement uncertainty. At high flow angles, greater than 30 deg, where flow may separate from the leeward side of the probe, the direct-interpolation method was preferable. At low flow angles, less than 30 deg, where flow remains attached about the probe, neither data-reduction method had any advantage. For both methods, a calibration grid with a maximum interval of 10 deg was recommended. The Reynolds-number sensitivity of the probe began at Re=5000, based on probe diameter, and was independent of the data-reduction method or calibration grid spacing.

scholarly journals Analytical corrections for double-cantilever beam tests

2021 ◽  
Author(s):  
T. Chen ◽  
C. M. Harvey ◽  
S. Wang ◽  
V. V. Silberschmidt
Keyword(s):  
Elastic Foundation ◽  
Analytical Solutions ◽  
Data Reduction ◽  
Crack Tip ◽  
Beam Theory ◽  
Dynamic Loads ◽  
Structural Vibration ◽  
Mode I ◽  
Mode I Fracture ◽  
Reduction Methods

AbstractDouble-cantilever beams (DCBs) are widely used to study mode-I fracture behavior and to measure mode-I fracture toughness under quasi-static loads. Recently, the authors have developed analytical solutions for DCBs under dynamic loads with consideration of structural vibration and wave propagation. There are two methods of beam-theory-based data reduction to determine the energy release rate: (i) using an effective built-in boundary condition at the crack tip, and (ii) employing an elastic foundation to model the uncracked interface of the DCB. In this letter, analytical corrections for a crack-tip rotation of DCBs under quasi-static and dynamic loads are presented, afforded by combining both these data-reduction methods and the authors’ recent analytical solutions for each. Convenient and easy-to-use analytical corrections for DCB tests are obtained, which avoid the complexity and difficulty of the elastic foundation approach, and the need for multiple experimental measurements of DCB compliance and crack length. The corrections are, to the best of the authors’ knowledge, completely new. Verification cases based on numerical simulation are presented to demonstrate the utility of the corrections.

In situ isotopic analysis of uranium using a new data acquisition protocol for 1013 ohm Faraday amplifiers

2021 ◽  
Author(s):  
Kota Yamamoto ◽  
Hisashi Asanuma ◽  
Hiroaki Takahashi ◽  
Takafumi Hirata
Keyword(s):  
Data Acquisition ◽  
Data Reduction ◽  
Reduction Method ◽  
Isotopic Analysis ◽  
High Gain ◽  
Transient Signals ◽  
Acquisition Protocol ◽  
Data Reduction Method ◽  
Isotopic Measurements

New data reduction method for isotopic measurements using high-gain Faraday amplifiers enables precise uranium isotopic analysis even from transient signals.

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

scholarly journals Evaluation of the Accuracy of Bathymetry on the Nearshore Coastlines of Western Korea from Satellite Altimetry, Multi-Beam, and Airborne Bathymetric LiDAR

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2926 ◽  
Author(s):  
Yeon Yeu ◽  
Jurng-Jae Yee ◽  
Hong Yun ◽  
Kwang Kim
Keyword(s):  
Disaster Preparedness ◽  
Satellite Altimetry ◽  
Gravity Anomalies ◽  
Side Scan Sonar ◽  
Single Beam ◽  
Bathymetric Data ◽  
Depth Data ◽  
Sonar Sensors ◽  
Beam Depth ◽  
Territory Management

Bathymetric mapping is traditionally implemented using shipborne single-beam, multi-beam, and side-scan sonar sensors. Procuring bathymetric data near coastlines using shipborne sensors is difficult, however, this type of data is important for maritime safety, marine territory management, climate change monitoring, and disaster preparedness. In recent years, the bathymetric light detection and ranging (LiDAR) technique has been tried to get seamless geospatial data from land to submarine topography. This paper evaluated the accuracy of bathymetry generated near coastlines from satellite altimetry-derived gravity anomalies and multi-beam bathymetry using a tuning density contrast of 5000 kg/m3 determined by the gravity-geologic method. Comparing with the predicted bathymetry of using only multi-beam depth data, 78% root mean square error from both multi-beam and airborne bathymetric LiDAR was improved in shallow waters of nearshore coastlines of the western Korea. As a result, the satellite-derived bathymetry estimated from the multi-beam and the airborne bathymetric LiDAR was enhanced to the accuracy of about 0.2 m.

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