Issues in Data Selection and Description of Data

Language is a tool used to convey ideas, ideas, feelings, and thoughts to others. Language, which is an arbiter sound symbol system that is used by members of one community to work together, interact, and identify themselves. In other words, language can be a means to convey intent or purpose so that the desired thing is achieved. As a communication tool, language develops over time. Therefore, language will never die if its speakers still care and love for the language comparative linguistics as a study or study of language which includes the comparison of cognate languages or the historical development of a language. This research aims to elucidate the use of Indonesian language at students of Iqra Buru University with Comparative Analysis. The method used in this research is descriptive with a qualitative approach. Data collected by observation, interview, and documentation techniques. Sources of data in this study were 5 speakers 5 speakers language Analyzing the data by means of data selection, data classification, and data presentation. The results of the study stated that there were phonological differences, namely the Jamee vowel system contained / ɛ / and / ɔ /, while the vocal system was / a / and the consonant system was Jamee / ɣ / or / R /, while the language / r /, then the difference pronunciations include single vowels, for example makɛn, bɔRa in Jamee and eating, bara in Language used by iqra Rush university students. Keywords

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Data selection for naturalness in HMM-based speech synthesis

10.21437/speechprosody.2016-162 ◽

2016 ◽

Cited By ~ 1

Author(s):

Erica Cooper ◽

Yocheved Levitan ◽

Julia Hirschberg

Keyword(s):

Speech Synthesis ◽

Data Selection ◽

Selection For

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Pushing the limits of solubility prediction via quality-oriented data selection

iScience ◽

10.1016/j.isci.2020.101961 ◽

2021 ◽

Vol 24 (1) ◽

pp. 101961

Author(s):

Murat Cihan Sorkun ◽

J.M. Vianney A. Koelman ◽

Süleyman Er

Keyword(s):

Data Selection ◽

Solubility Prediction

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Failure modeling in a gas turbine system: Combining classification with anomaly detection models for two data selection strategies

2020 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci47803.2020.9308271 ◽

2020 ◽

Author(s):

Catherine Cheung ◽

Davis To ◽

Julio Valdes

Keyword(s):

Anomaly Detection ◽

Gas Turbine ◽

Data Selection ◽

Failure Modeling ◽

Selection Strategies

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Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites

Earth Planets and Space ◽

10.1186/s40623-021-01357-9 ◽

2021 ◽

Vol 73 (1) ◽

Cited By ~ 1

Author(s):

Magnus D. Hammer ◽

Grace A. Cox ◽

William J. Brown ◽

Ciarán D. Beggan ◽

Christopher C. Finlay

Keyword(s):

Time Series ◽

Secular Variation ◽

Geomagnetic Field ◽

Time Windows ◽

Low Earth Orbit ◽

Data Selection ◽

Spherical Harmonic Analysis ◽

Core Field ◽

The Core ◽

Swarm Satellites

AbstractWe present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites.

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