Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process

Stateczny, Andrzej; Wlodarczyk–Sielicka, Marta

doi:10.1007/978-3-319-08729-0_34

Cited by 42 publications

(26 citation statements)

References 21 publications

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“…During the use of Kohonen's self-organizing maps, the network, during learning, use the WTM rule -rule, which modifies not only the weight of the winner, but also its neighbors. The hexagonal topology is selected (each of the hexagons represents a neuron) and the number of epochs is set at 200 based on recent research [22]. The numbers of rows and columns are set to 5x5 and 3x3, which provide the number of neurons 25 and 9.…”

Section: The Experimentsmentioning

confidence: 99%

Selection of SOM parameters for the needs of clusterization of data obtained by interferometric methods

Wlodarczyk–Sielicka

Stateczny²

2015

2015 16th International Radar Symposium (IRS)

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The article presents a detailed analysis of parameter settings of self-organizing map (SOM) for the clusterization of bathymetric data obtained using interferometric techniques. Clusterization using SOM is one of the stages of a new a geodata reduction method being currently researched by the authors for the purpose of a bathymetric map construction. In the research authors used data obtained by GeoSwath+.-interferometric sonar system. Test data gathered from the area of 100m² included 3760 data points. During the tests the authors focused primarily on setting individual network parameters in the course of network training and also on their importance in the clustering of bathymetric data. A total of forty-eight different scenarios of SOM parameter settings were tested. In the article detailed analysis of the obtained results is presented with an emphasis on the use of SOM in future studies related to new geodata reduction method.

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Section: The Experimentsmentioning

confidence: 99%

Selection of SOM parameters for the needs of clusterization of data obtained by interferometric methods

Wlodarczyk–Sielicka

Stateczny²

2015

2015 16th International Radar Symposium (IRS)

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“…Data mining and machine learning algorithms are another variant of big data reduction methods [14,42,43]. These methods process the data streams by performing supervised, unsupervised, semi-supervised and deep learning models.…”

Section: Related Workmentioning

confidence: 99%

RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

Rehman

Jayaraman

Malik

et al. 2017

JSAN

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Abstract:We are witnessing the emergence of new big data processing architectures due to the convergence of the Internet of Things (IoTs), edge computing and cloud computing. Existing big data processing architectures are underpinned by the transfer of raw data streams to the cloud computing environment for processing and analysis. This operation is expensive and fails to meet the real-time processing needs of IoT applications. In this article, we present and evaluate a novel big data processing architecture named RedEdge (i.e., data reduction on the edge) that incorporates mechanism to facilitate the processing of big data streams near the source of the data. The RedEdge model leverages mobile IoT-termed mobile edge devices as primary data processing platforms. However, in the case of the unavailability of computational and battery power resources, it offloads data streams in nearer mobile edge devices or to the cloud. We evaluate the RedEdge architecture and the related mechanism within a real-world experiment setting involving 12 mobile users. The experimental evaluation reveals that the RedEdge model has the capability to reduce big data stream by up to 92.86% without compromising energy and memory consumption on mobile edge devices.

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“…The number of epochs was set at 200 based on recent research (Stateczny and Wlodarczyk-Sielicka, 2014). The total yield of 24 different sets of clusters was analysed.…”

Section: Lu S T E R I N G B At H Y M E T R I C Data F O R E N C Smentioning

confidence: 99%

Clustering Bathymetric Data for Electronic Navigational Charts

Wlodarczyk–Sielicka

Stateczny²

2016

J. Navigation

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An electronic navigational chart is a major source of information for the navigator. The component that contributes most significantly to the safety of navigation on water is the information on the depth of an area. For the purposes of this article, the authors use data obtained by the interferometric sonar GeoSwath Plus. The data were collected in the area of the Port of Szczecin. The samples constitute large sets of data. Data reduction is a procedure to reduce the size of a data set to make it easier and more effective to analyse. The main objective of the authors is the compilation of a new reduction algorithm for bathymetric data. The clustering of data is the first part of the search algorithm. The next step consists of generalisation of bathymetric data. This article presents a comparison and analysis of results of clustering bathymetric data using the following selected methods: K-means clustering algorithm, traditional hierarchical clustering algorithms and self-organising map (using artificial neural networks). K E Y WO R D S1. Electronic Chart Display and Information System. 2. Bathymetry. 3. Data reduction.

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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process

Cited by 42 publications

References 21 publications

Selection of SOM parameters for the needs of clusterization of data obtained by interferometric methods

Selection of SOM parameters for the needs of clusterization of data obtained by interferometric methods

RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

Clustering Bathymetric Data for Electronic Navigational Charts

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