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Record W2141140125 · doi:10.5555/1898953.1899015

A dependable infrastructure of the electric network for e-textiles

2006· article· en· W2141140125 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

VenueInternational Parallel and Distributed Processing Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsBattery (electricity)Computer scienceWearable computerElectrical engineeringFault (geology)Power (physics)Electric powerEmbedded systemEngineering

Abstract

fetched live from OpenAlex

Electronic textiles, known as computational fabrics, offer an emerging method for constructing wearable and large area applications. Because e-textiles are battery-driven and fault-prone systems, there is a need for developing a dependable infrastructure of the electric networks for e-textiles. In this paper, a new infrastructure of the power networks for e-textiles, flexible power network (FPN), is presented. Instead of drawing power from a fixed battery as in the conventional electric networks, the power consuming nodes in a FPN can obtain power energy from one of the choices of batteries available with the help of the battery selectors. We also introduce the over current protectors into the battery nodes (BN) to protect the batteries from wasting the charge when short-circuit faults occur. The electric features of battery selectors and over current protectors, the two types of important electric devices used in FPNs, are illustrated in the paper. We have performed simulation experiments and the results show that our FPNs are more dependable than some common electric networks published before in the cases of short- and open-circuit faults.

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 score0.318
Threshold uncertainty score0.319

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 communication0.0000.000
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 head0.004
GPT teacher head0.204
Teacher spread0.199 · 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