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Record W2137672456 · doi:10.1155/2014/825726

Research on Wavelet Singularity Detection Based Fault-Tolerant Federated Filtering Algorithm for INS/GPS/DVL Integrated Navigation System

2014· article· en· W2137672456 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Mathematics · 2014
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsNavigation systemGlobal Positioning SystemAlgorithmFault (geology)Computer scienceWaveletMaxima and minimaSIGNAL (programming language)Fault detection and isolationReliability (semiconductor)Real-time computingMathematicsArtificial intelligencePhysicsTelecommunicationsMathematical analysis

Abstract

fetched live from OpenAlex

Soft faults in navigation sensors will lead to the degradation of the accuracy and reliability of integrated navigation system. To solve this problem, a wavelet analysis and signal singularities based soft fault detection method are given out. To find signal singularities and detect the faults, the modulus maxima values are calculated after the wavelet transform of original signal. By calculating the Lipschitz exponent using the modulus maxima value at the fault point, the fault types are distinguished. Then, a fault-tolerant federated filtering algorithm for the calibration of INS/GPS/DVL integrated navigation system is proposed. Simulations are conducted and results show that sensor soft faults can be detected accurately. By effectively isolating the fault and refactoring information, the accuracy and reliability of navigation system are improved.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.273
Teacher spread0.249 · 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