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Record W4407865510 · doi:10.23977/jeis.2025.100103

Synchronous detection system for temperature and strain in partial discharges of three-phase cables based on FBG and neural networks

2025· article· en· W4407865510 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electronics and Information Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsnot available
FundersHenan Polytechnic University
KeywordsStrain (injury)Phase (matter)Materials scienceArtificial neural networkComputer sciencePhysicsArtificial intelligenceBiologyAnatomy

Abstract

fetched live from OpenAlex

To detect the partial discharge (PD) faults in three-phase cross-linked polyethylene (XLPE) cable joints, this paper designs a parallel sensing detection system based on fiber Bragg gratings (FBG). By measuring the changes in the reflected power of FBGs in each branch and combining with a Back Propagation (BP) neural network algorithm, the demodulation of temperature and strain during PD is achieved. To verify the feasibility of this system, vibration signals and temperature changes are respectively applied to each FBG, and simulation experiments are carried out. The experimental results show that this system can accurately detect the temperature changes, the frequencies of vibration signals, and the strains in each branch, verifying its feasibility for detecting cable joint faults. In addition, by adjusting the sampling frequency of the photodetector, higher-frequency vibration signals can be measured.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.221
Teacher spread0.218 · 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