scholarly journals An analysis of test data selection criteria using the RELAY model of fault detection

1993 ◽  
Vol 19 (6) ◽  
pp. 533-553 ◽  
Author(s):  
D.J. Richardson ◽  
M.C. Thompson
Keyword(s):  
Fault Detection ◽  
Test Data ◽  
Selection Criteria ◽  
Data Selection

scholarly journals Accelerating assimilation development for new observing systems using EFSO

2018 ◽  
Vol 25 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Daisuke Hotta ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi ◽  
Tse-Chun Chen
Keyword(s):  
Selection Criteria ◽  
Ensemble Forecast ◽  
Data Selection ◽  
Development Work ◽  
Observation Data ◽  
Precipitation Data ◽  
Trial And Error ◽  
Satellite Precipitation ◽  
Selection Strategies ◽  
Observing Systems

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

Improved Training for Machine Learning: The Additional Potential of Innovative Algorithmic Approaches.

2021 ◽  
Author(s):  
Octavian Dumitru ◽  
Gottfried Schwarz ◽  
Mihai Datcu ◽  
Dongyang Ao ◽  
Zhongling Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Test Data ◽  
Satellite Images ◽  
Training Data ◽  
Data Selection ◽  
Generative Adversarial Networks ◽  
Radar Images ◽  
Basic Work ◽  
Selection Of

<p>During the last years, much progress has been reached with machine learning algorithms. Among the typical application fields of machine learning are many technical and commercial applications as well as Earth science analyses, where most often indirect and distorted detector data have to be converted to well-calibrated scientific data that are a prerequisite for a correct understanding of the desired physical quantities and their relationships.</p><p>However, the provision of sufficient calibrated data is not enough for the testing, training, and routine processing of most machine learning applications. In principle, one also needs a clear strategy for the selection of necessary and useful training data and an easily understandable quality control of the finally desired parameters.</p><p>At a first glance, one could guess that this problem could be solved by a careful selection of representative test data covering many typical cases as well as some counterexamples. Then these test data can be used for the training of the internal parameters of a machine learning application. At a second glance, however, many researchers found out that a simple stacking up of plain examples is not the best choice for many scientific applications.</p><p>To get improved machine learning results, we concentrated on the analysis of satellite images depicting the Earth’s surface under various conditions such as the selected instrument type, spectral bands, and spatial resolution. In our case, such data are routinely provided by the freely accessible European Sentinel satellite products (e.g., Sentinel-1, and Sentinel-2). Our basic work then included investigations of how some additional processing steps – to be linked with the selected training data – can provide better machine learning results.</p><p>To this end, we analysed and compared three different approaches to find out machine learning strategies for the joint selection and processing of training data for our Earth observation images:</p><ul><li>One can optimize the training data selection by adapting the data selection to the specific instrument, target, and application characteristics [1].</li> <li>As an alternative, one can dynamically generate new training parameters by Generative Adversarial Networks. This is comparable to the role of a sparring partner in boxing [2].</li> <li>One can also use a hybrid semi-supervised approach for Synthetic Aperture Radar images with limited labelled data. The method is split in: polarimetric scattering classification, topic modelling for scattering labels, unsupervised constraint learning, and supervised label prediction with constraints [3].</li> </ul><p>We applied these strategies in the ExtremeEarth sea-ice monitoring project (http://earthanalytics.eu/). As a result, we can demonstrate for which application cases these three strategies will provide a promising alternative to a simple conventional selection of available training data.</p><p>[1] C.O. Dumitru et. al, “Understanding Satellite Images: A Data Mining Module for Sentinel Images”, Big Earth Data, 2020, 4(4), pp. 367-408.</p><p>[2] D. Ao et. al., “Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X”, Remote Sensing, 2018, 10(10), pp. 1-23.</p><p>[3] Z. Huang, et. al., "HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Images", IEEE Transactions on Geoscience and Remote Sensing, 2020, pp.1-18.</p>

The Data Selection Criteria for HSC and SVM Algorithms

2008 ◽  
Author(s):  
Qing He ◽  
Fuzhen Zhuang ◽  
Zhongzhi Shi
Keyword(s):  
Selection Criteria ◽  
Data Selection

N-N-N∗ model calculations for experimental η-mesic 3He searches

2019 ◽  
Vol 28 (08) ◽  
pp. 1950066 ◽  
Author(s):  
N. G. Kelkar ◽  
H. Kamada ◽  
M. Skurzok
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Momentum Distribution ◽  
Selection Criteria ◽  
Data Selection ◽  
Model Calculations ◽  
Faddeev Equations ◽  
Essential Ingredient ◽  
Momentum Distributions ◽  
Three Body

The possibility for the existence of the exotic [Formula: see text] states is explored with the objective of calculating the [Formula: see text] momentum distribution inside such nuclei. Even though the latter is an essential ingredient for the analysis of the experimental data on the [Formula: see text], [Formula: see text] and [Formula: see text] reactions aimed at finding an [Formula: see text]-mesic 3He, the data analysis is usually performed by approximating the [Formula: see text] momentum distribution by that of a nucleon. Here, we present calculations performed by solving the three-body Faddeev equations to obtain the momentum distribution of the [Formula: see text] inside possible ([Formula: see text])[Formula: see text], ([Formula: see text])[Formula: see text] and ([Formula: see text])[Formula: see text]-[Formula: see text] states. The [Formula: see text] momentum distributions are found to be much narrower than those of the nucleons and influence the data selection criteria.

Processing of noisy magnetotelluric data using digital filters and additional data selection criteria

1988 ◽  
Vol 52 (1-2) ◽  
pp. 30-40 ◽  
Author(s):  
S.L. Fontes ◽  
T. Harinarayana ◽  
G.J.K. Dawes ◽  
V.R.S. Hutton
Keyword(s):  
Selection Criteria ◽  
Digital Filters ◽  
Additional Data ◽  
Data Selection ◽  
Magnetotelluric Data

A method for test data selection

1987 ◽  
Vol 7 (2) ◽  
pp. 89-97 ◽  
Author(s):  
F.R.D. Velasco
Keyword(s):  
Test Data ◽  
Data Selection

A Novel Approach to Test Data Selection in Regression Testing of Software

2010 ◽  
Vol 1 (14) ◽  
pp. 61-66
Author(s):  
Soubhagya Sankar Barpanda ◽  
Durga Prasad Mohapatra ◽  
Baikuntha Narayan Biswal
Keyword(s):  
Test Data ◽  
Regression Testing ◽  
Data Selection ◽  
Novel Approach
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