scholarly journals GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys

2021 ◽  
Author(s):  
M. A. Álvarez ◽  
C. Dafonte ◽  
M. Manteiga ◽  
D. Garabato ◽  
R. Santoveña
Keyword(s):  
European Space Agency ◽  
Unsupervised Classification ◽  
Analysis Tool ◽  
Visualization Tool ◽  
Self Organizing Maps ◽  
Astronomical Surveys ◽  
Space Agency ◽  
The One ◽  
Adaptive Visualization ◽  
Self Organizing

AbstractWe present an adaptive visualization tool for unsupervised classification of astronomical objects in a Big Data context such as the one found in the increasingly popular large spectrophotometric sky surveys. This tool is based on an artificial intelligence technique, Kohonen’s self-organizing maps, and our goal is to facilitate the analysis work of the experts by means of oriented domain visualizations, which is impossible to achieve by using a generic tool. We designed a client-server that handles the data treatment and computational tasks to give responses as quickly as possible, and we used JavaScript Object Notation to pack the data between server and client. We optimized, parallelized, and evenly distributed the necessary calculations in a cluster of machines. By applying our clustering tool to several databases, we demonstrated the main advantages of an unsupervised approach: the classification is not based on pre-established models, thus allowing the “natural classes” present in the sample to be discovered, and it is suited to isolate atypical cases, with the important potential for discovery that this entails. Gaia Utility for the Analysis of self-organizing maps is an analysis tool that has been developed in the context of the Data Processing and Analysis Consortium, which processes and analyzes the observations made by ESA’s Gaia satellite (European Space Agency) and prepares the mission archive that is presented to the international community in sequential periodic publications. Our tool is useful not only in the context of the Gaia mission, but also allows segmenting the information present in any other massive spectroscopic or spectrophotometric database.

scholarly journals Applicability of NGGM near-real time simulations in flood detection

2019 ◽  
Vol 9 (1) ◽  
pp. 111-126
Author(s):  
A. F. Purkhauser ◽  
J. A. Koch ◽  
R. Pail
Keyword(s):  
European Space Agency ◽  
Earth System ◽  
The Earth ◽  
Future Mission ◽  
Space Agency ◽  
Flood Detection ◽  
The One ◽  
Real Time Simulations ◽  
Grace Mission ◽  
Gravity Mission

Abstract The GRACE mission has demonstrated a tremendous potential for observing mass changes in the Earth system from space for climate research and the observation of climate change. Future mission should on the one hand extend the already existing time series and also provide higher spatial and temporal resolution that is required to fulfil all needs placed on a future mission. To analyse the applicability of such a Next Generation Gravity Mission (NGGM) concept regarding hydrological applications, two GRACE-FO-type pairs in Bender formation are analysed. The numerical closed loop simulations with a realistic noise assumption are based on the short arc approach and make use of the Wiese approach, enabling a self-de-aliasing of high-frequency atmospheric and oceanic signals, and a NRT approach for a short latency. Numerical simulations for future gravity mission concepts are based on geophysical models, representing the time-variable gravity field. First tests regarding the usability of the hydrology component contained in the Earth System Model (ESM) by the European Space Agency (ESA) for the analysis regarding a possible flood monitoring and detection showed a clear signal in a third of the analysed flood cases. Our analysis of selected cases found that detection of floods was clearly possible with the reconstructed AOHIS/HIS signal in 20% of the tested examples, while in 40% of the cases a peak was visible but not clearly recognisable.

Self-Organizing Maps Applied as Analysis Tool of Reading Cognitive Test

2018 ◽  
Vol 16 (6) ◽  
pp. 1817-1824
Author(s):  
J. M. Jarske ◽  
A. G. Seabra ◽  
L. A. Silva
Keyword(s):  
Cognitive Test ◽  
Analysis Tool ◽  
Self Organizing Maps ◽  
Self Organizing

A New Method for Emulating Self-Organizing Maps for Visualization of Datasets

2018 ◽  
Vol 17 (03) ◽  
pp. 1850014 ◽  
Author(s):  
Macario O. Cordel ◽  
Arnulfo P. Azcarraga
Keyword(s):  
Disaster Response ◽  
Dimensional Space ◽  
Main Idea ◽  
Cost Effective ◽  
Fine Tuning ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Text Complexity ◽  
Self Organizing Maps ◽  
Self Organizing

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with [Formula: see text] complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with [Formula: see text] complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking

2003 ◽  
Vol 2 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Tomas Eklund ◽  
Barbro Back ◽  
Hannu Vanharanta ◽  
Ari Visa
Keyword(s):  
Performance Analysis ◽  
Pulp And Paper ◽  
The Self ◽  
Financial Data ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Period ◽  
The World ◽  
Self Organizing

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

scholarly journals Winner-Relaxing Self-Organizing Maps

2005 ◽  
Vol 17 (5) ◽  
pp. 996-1009 ◽  
Author(s):  
Jens Christian Claussen
Keyword(s):  
Information Theory ◽  
Dimensional Case ◽  
Self Organizing Maps ◽  
One Dimensional ◽  
New Family ◽  
Wide Range ◽  
The One ◽  
Self Organizing

A new family of self-organizing maps, the winner-relaxing Kohonen algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behavior is calculated analytically. For the original variant, a magnification exponent of 4/7 is derived; the generalized version allows steering the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional case, thus providing optimal mapping in the sense of information theory. The winner-relaxing algorithm requires minimal extra computations per learning step and is conveniently easy to implement.

scholarly journals Science Observations with the IUE Using the One Gyro Mode

1990 ◽  
Vol 123 ◽  
pp. 517-520
Author(s):  
C. Imhoff ◽  
R. Pitts ◽  
R. Arquilla ◽  
C. Shrader ◽  
M. Perez ◽  
...  
Keyword(s):  
Control System ◽  
Attitude Control ◽  
European Space Agency ◽  
Attitude Control System ◽  
Sun Sensor ◽  
Space Agency ◽  
Current Attitude ◽  
International Ultraviolet Explorer ◽  
The One ◽  
Science Instrument

AbstractThe International Ultraviolet Explorer (IUE) is a geosynchronous orbiting telescope launched by the National Aeronautics and Space Administration (NASA) on January 26, 1978, and operated jointly by NASA and the European Space Agency. The science instrument consists of two spectrographs which span the wavelength range of 1150 to 3200 Å and offer two dispersions with resolutions of 6 Å and 0.2 Å. The spacecraft’s attitude control system originally included an inertial reference package containing 6 gyroscopes for 3-axis stabilization. The science instrument includes a prime and redundant Field Error Sensor (FES) camera for target aquisition and offset guiding. Since launch, 4 of the 6 gyroscopes have failed. The current attitude control system utilizes the remaining 2 gyros and a Fine Sun Sensor (FSS) for 3-axis stabilization. When the next gyro fails, a new attitude control system will be uplinked which will rely on the remaining gyro and the FSS for general 3-axis stabilzation. In addition to the FSS, the FES cameras will be required to assist in maintaining fine attitude control during target aquisition. This has required thoroughly determining the characteristics of the FES cameras and the spectrograph aperture plate as well as devising new target acquisition procedures. The results of this work are presented.

THE COMBINED USE OF SELF-ORGANIZING MAPS AND ANDREWS' CURVES

2005 ◽  
Vol 15 (03) ◽  
pp. 197-206 ◽  
Author(s):  
C. GARCÍA-OSORIO ◽  
C. FYFE
Keyword(s):  
Data Sets ◽  
Analysis Tool ◽  
Use Of Self ◽  
Process Data ◽  
Self Organizing Maps ◽  
Combined Use ◽  
Exploratory Data ◽  
Data Analysis Tool ◽  
Analyze Data ◽  
Self Organizing

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to perform that visualization using a variant of Andrews' Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters. Perhaps more importantly, using the SOM to pre-process data by identifying gross features enables us to use Andrews' Curves on data sets which would have previously been too large for the methodology. Finally we show how a three way interaction between the human user and these two methods can be a valuable exploratory data analysis tool.

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