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Record W4394564090 · doi:10.1109/icjece.2024.3374951

Parity-Time-Symmetric Wireless Power Transfer System Based on Self-Excited Converter

2024· article· en· W4394564090 on OpenAlexaffvenue
Jingjing Yang, Mengling Li, Shumin Ran, Jason Gu

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsDalhousie University
FundersNatural Science Foundation of Fujian Province
KeywordsCorrectnessConvertersRobustness (evolution)Control theory (sociology)Nonlinear systemComputer scienceMaximum power transfer theoremElectric power systemLyapunov functionWireless power transferWirelessElectronic engineeringPower (physics)EngineeringElectrical engineeringVoltagePhysicsTelecommunicationsControl (management)Algorithm

Abstract

fetched live from OpenAlex

The nonlinear parity-time-symmetric wireless power transfer (PT-WPT) system has garnered significant attention for its robustness against variations in the coupling coefficient. Currently, the implementation of nonlinear negative resistance primarily relies on switch-mode converters. Among these, the conventional PT-WPT system based on self-excited converters faces challenges such as limited output power and overall system efficiency (OSE). In this article, we propose a novel dual-supply self-excited PT-WPT system designed to address these challenges, offering a cost-effective solution. We present the circuit, coupled-mode model, and PT-symmetric condition of the proposed system. Furthermore, stability criteria of the system at each equilibrium point are analyzed using Lyapunov’s first method. Finally, we implement a single-supply system to validate the correctness of the models and stability criteria. In addition, a dual-supply system is constructed, achieving an output power of 101.2 W with OSE of 89.2%.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
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
Bibliometrics0.0010.001
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 head0.003
GPT teacher head0.145
Teacher spread0.142 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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