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Record W2071146428 · doi:10.5539/ies.v4n1p122

Applying the Multisim Technology to Teach the Course of High Frequency Power Amplifier

2011· article· en· W2071146428 on OpenAlexvenueno aff
Gang Lv, Yuan-Sheng Xue

Bibliographic record

VenueInternational Education Studies · 2011
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
FundersSoochow University
KeywordsComputer scienceGRASPAmplifierClass (philosophy)Function (biology)Course (navigation)SoftwareTeaching methodElectrical engineeringQuality (philosophy)Power (physics)Mathematics educationElectronic engineeringEngineeringPsychologyCMOSSoftware engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

As one important professional base course in the electric information specialty, the course of “high frequency electronic circuit” has strong theoretical characteristic and abstract content. To enhance the teaching quality of this course, the computer simulation technology based on Multisim is introduced into the teaching of “high frequency electronic circuit”, which could not only deepen students’ understanding and memory of basic concepts, but also effectively help students to understand and grasp the principle of the high frequency circuit by the circuit design module and the simulation analysis function. The teaching application effect of the high frequency class C power amplifier has also proved that the Multisim software could realize the organic combination of theory and practice, and it is the profitable supplement of the traditional teaching mode.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.077
GPT teacher head0.429
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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