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Record W3089120364 · doi:10.15407/fmmit2020.30.071

Orthogonal transformations and moments in digital information processing

2020· article· en· W3089120364 on OpenAlexaboutno aff
Yaroslav Pyanylo, Mykhailo Petrus, Andriy Demichkovsky

Bibliographic record

VenuePhysico-mathematical modelling and informational technologies · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAdvanced Scientific Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsCombinatoricsPhysicsStatistics

Abstract

fetched live from OpenAlex

The method of application of the theory of spectral schedules, theory of moments and statistical-probabilistic methods of processing of the digital information received at preparation of sportsmen for competitive activity, in particular at data processing of heart rate (heart rate) is offered in the work. The paper shows the relationship of the moments of the distribution function with the generalized spectra of orthogonal decompositions, considers the question of determining the sample size to obtain research results. References Pianylo, Ya. D. (2011). Proektsiino-iteratsiini metody rozviazuvannia priamykh ta obernenykh zadach perenosu. Lviv: Splain. Bat, M. (1980). Spektralnyi analiz v geofizike. Moskva. Nedra. Gold, B., Reider, CH. (1973). TSifrovaia obrabotka signalov. Moskva. Sov. radio. Suetin, P. K. (2005). Klassicheskie ortogonalnye mnogochleny 3-e izd. pererab. i dop. M. FIZMATLIT. Dzhekson, D. (1948). Riady fure i ortogonalnye polinomy. Perevod s angliiskogo. Gosudarstvennoe izdatelstvo inostrannoi literatury. Matsko, I. Y., Yavorskyi, I. M., Yuzefovych, R. M., Semenov, P. O. (2018). Statystychnyi vektorno-tenzornyi analiz vibratsii tsentryfuhy z rozvynutym defektom obertovoho vuzla. Fizyko-khimichna mekhanika materialiv, 2, 140-147. Morozova, H. V., Sukharkova, O. I. (2012). Identyfikatsiia fihur na ploshchyni za dopomohoiu tsentralnykh momentiv yikh zobrazhen. Prykladna heometriia ta inzhenerna hrafika, 90, 200-205. Avramenko, V. I., Karimov, I. K. (2013). Teoriia ymovirnostei i matematychna statystyka : navch. posibnyk 2-he vyd., pererob. i dop.Dniprodzerzhynsk : DDTU. Gilmore, J. F., Pemberton, W. B. (1984). A suivery of aircraft classification algorithms. 7th Int. Conf. On PR., Montreal, 559-562. Wolf, S., Louvion, J. R. (1979). Considerations sur les formes: pseudo symetrie representation. 2e congres AFCET-IRIA/ Reconnassance des Formes et Intelligence Artificielle, Toulouse. 381-387. Dudani, J. A., Breeding, K. J. (1997). Aircraft identification by moments invariants. IEEE Trans. On Computers. Hilmor, Dzh. F., Pemberton, W. B. Nabir alhorytmiv klasyfikatsii litakiv. 7th Int. Konf. Pro PR., Monreal, 559-562. Volf, S., Luvion, Dzh. R. (1979). Rozghliad sur les formes: psevdosymetrychne predstavlennia. 2e konhresy AFCET-IRIA / Reconnassance des Formes et Intelligence Artificielle, Tuluza. 381-387. Dudani, Zh. A. (1997). Identyfikatsiia litaka za momentamy invariantiv. IEEE Trans. Pro kompiutery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.282
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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