Electroluminescent optical fiber sensor for detection of a high intensity electric field
Bibliographic record
Abstract
On-line testing of high power electromagnetic devices is one of the most important problems of modern industrial metrology. In the paper, the results of experimental investigations of the electric field optical fiber sensor based on the electroluminescent phenomena are presented. The electro¬luminescent effect is observed in some composite semicon¬ductors, among others in zinc sulfide ZnS crystals. In our investigations, the used ZnS crystal was doped with copper Cu atoms as activators. The concentration of activator in the ZnS crystal was about 5.10-4 [g/g]. According to plans of investi¬gations of the elaborated electroluminescent sensor, the spectral properties as well as the intensity of light emission in sinusoidal alternating electric field were tested.Full Text: PDF References:K.T.V. Grattan, Fiber Optic Fluorescence Thermometry, Chapman and Hall, London, 1996 [CrossRef]K. Kyuma, S. Tai, T. Sawada, "Fiber-optic instrument for temperature measurement", J. Quntum. Electronics, 73(3), 1997 [CrossRef]A. Brief, J. Chem. Educ., 88(6), 731 (2011). [CrossRef]T. Pustelny, B. Pustelny, "Investigation of electroluminophores for their practical application in optical fibre sensor technology", Opto-Electronics Rev.,10(3), 193 (2002). [CrossRef]A.Wrzesinska, Photo- and electroluminophore, Wroclaw, PWN Press, 1988, (in polish) [DirectLink]K.A. Franz, W.G. Kehr, "Luminescent Materials", Ullmans Encyclopedie of Industral Chemistry, Wiley-VCH, Veinhen, 2008 [CrossRef]A.G. Milnes, Deep Impurities In Semiconductors, A Willey-Interscience Publication, Toronto, 1993 [DirectLink]M. Aven, J.S. Prener, Physics and Chemistry of II-VI Compounds, North-Holland Publishing Company - Amsterdam, 1993 [DirectLink]P.K. Cheo, Fiber Optics Devices and Systems, Prentice-Hall, 1985 [CrossRef]D. Randall, Fluorescence and Phosphorescence, Grown, Oxford, 2007. [CrossRef]M. Koen, Photoconductivity of Semiconductors, Edited by Parks, New York, 1996 [CrossRef]K.R. Murphy, C.A. Stedmon, Annal. Methods, 6(3), 658, (2014) [CrossRef]T. Pustelny, K. Barczak, K. Gut, J. Wojcik, "Special optical fiber type D applied in optical sensor of electric currents", Optica Applicata, 34(4), 531 (2004). [DirectLink]K. Barczak, T. Pustelny, D. Dorosz, J. Dorosz, "Polarization maintaining fibers for application in magnetic field measurements", Europ. Phys. Journal: S.T., 154, 11, (2008) [CrossRef]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".