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Record W2888447045 · doi:10.1002/admt.201800105

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

2018· article· en· W2888447045 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

VenueAdvanced Materials Technologies · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTriboelectric effectEnergy harvestingComputer scienceMultidisciplinary approachAutomotive engineeringSystems engineeringEnergy (signal processing)EngineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract The quest to utilize intelligent tires has prompted substantial multidisciplinary research including vehicle dynamics, control, estimation, energy harvesting, and even nanotechnology. This review article presents the progress in the area of tire condition monitoring systems (TCMS) and intelligent tires using devices fabricated based on piezoelectric, electromagnetic, and triboelectric effects. Three main branches of the research in this area are presented, including estimation techniques, sensing, and energy harvesting approaches. The authors delineate the importance of TCMS for vehicle active safety systems, its importance for transportation safety, and also its remarkable economic and environmental impacts on the development of intelligent transportation systems. The historical evolution and the perspective of research in the area of intelligent tires and TCMS are also reviewed. In addition, it is discussed how nanogenerators would be effective on the future of intelligent tires.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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