MétaCan
Menu
Back to cohort
Record W2998598333 · doi:10.1109/iss46986.2019.8943660

HRG Crystal<sup>™</sup> DUAL CORE: Rebooting the INS revolution

2019· article· en· W2998598333 on OpenAlexaff
Y. Foloppe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsPhysicsTopology (electrical circuits)Electrical engineeringEngineering

Abstract

fetched live from OpenAlex

Safran Electronics & Defense’s hemispherical resonator gyroscope, the HRG Crystal™, has widely proven its ability to fulfill performances requirements usually reached by optical technologies (RLG and FOG), with the best C-SWaP1characteristics and much higher reliability (cf. DARPA latest study [5]).Based on HRG Crystal™technology, Safran Electronics & Defense has developed a new type of inertial core called HRG Crystal™DUAL CORE, able to reach higher performances similar to the ones achieved by ESG technology.Indeed, thanks to the unrivalled C-SWaP of HRG Crystal™for navigation grade gyro, the integration of multi sensors cores in a single cluster-core, made of 6 HRG Crystal™gyroscopes (instead of 3 for “standard” cores), and accelerometers, becomes possible.The DUAL CORE principle, patented by Safran Electronics & Defense, relies on resonator gyroscopes’ intrinsic characteristic: its self calibration capability. Thus, in a DUAL CORE, while 3 gyros are navigating, the 3 others are self calibrating, strongly increasing the inertial system’s accuracy.HRG Crystal™DUAL CORE allows Safran Electronics & Defense to address the most demanding applications with breakthrough products. For instance, in the naval market, Safran has launched at Euronaval 2018 the Black-Onyx™DUAL CORE, an INS for submarines that addresses the market with unreached performances: better than 0.5 NM/120h.There is no doubt that, in the near future, other defense sectors, like land and aeronautics, will be addressed with HRG Crystal™DUAL CORE inertial systems, as the need of extended performances, with better C-SWaP, becomes more and more significant each day.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations31
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicGeophysics and Sensor TechnologyFrench-language works237,207