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Record W4293704848 · doi:10.36227/techrxiv.20695966

A Single-Frequency Time-Reversal Method for Electromagnetic Source Reconstruction

2022· preprint· en· W4293704848 on OpenAlexaff
Juan Li, Jun Cai, ZHIZHANG CHEN, Xiaoyao Feng, Zhimeng Xu, Yuehe Ge, Yuande Yuan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsNarrowbandWidebandTime–frequency analysisComputer scienceImpulse (physics)Time domainAcousticsAlgorithmPhysicsTelecommunicationsOptics

Abstract

fetched live from OpenAlex

The time-reversal method has been applied to the source location due to its spatiotemporal focusing properties. Much work on the topic is to locate the impulse or wideband sources. However, most sources in practical situations are narrowband. Therefore, it is desirable to develop a single-frequency time-reversal method to reconstruct narrowband-source locations. Unlike the conventional time-reversal method, the single-frequency time-reversal method we propose in this paper extracts the field signals at a single frequency at the time-reversal-mirror locations and reinjects them into the solution domain for the backward simulations. The preliminary experimental results with an ergodic cavity demonstrate the effectiveness of our proposed method and move one crucial step forward for the practical uses of the time-reversal method. They lay the foundations for further extensions of time-reversal theory and applications.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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