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Record W2112931753

수음실 잔향 시간변화에 따른 바닥충격음레벨 특성

2013· article· ko· W2112931753 on OpenAlexaboutno aff
정정호, 김정욱, 정재군

Bibliographic record

VenueTransactions of the Korean Society for Noise and Vibration Engineering · 2013
Typearticle
Languageko
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsSound powerSound intensitySound pressureAcousticsReverberation roomSound intensity probeSound (geography)ReverberationElectromagnetic reverberation chamberAbsorption (acoustics)SoundproofingMaterials scienceCritical distancePhysics
DOInot available

Abstract

fetched live from OpenAlex

Field measurement method of heavy/soft impact sound pressure level which is regulated in JIS and ISO has been using in Korea, Japan and Canada. It is reported that heavy/soft impact sound pressure level was varied by the sound field condition of receiving room such as sound absorption power and room volume. In this study, it is checked that heavy/soft impact sound pressure level was affected by the receiving sound field condition. Rubber ball and bang machine sound pressure level was measured in the vertically connected reverberation chamber. In oder to check the effect of receiving sound field on heavy/soft impact sound pressure, sound absorption power was changed with polyester sound absorption blankets with air space and glass wool. The reverberation time at 1 kHz band was changed from 10 s to 0.2 s by sound absorption material. Rubber ball sound pressure level measured without sound absorption material was 58 dB in $L_{i,Fmax,AW}$, but the level was 46 dB with sound absorption treatment. From this result, it is confirmed that sound field correction method is needed in the heavy/soft impact sound pressure level measurement method using bang machine and rubber ball.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.210
Teacher spread0.202 · 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 designBench or experimental
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Korean Society for Noise and Vibration EngineeringSame topicEngineering Applied ResearchFrench-language works237,207