A Study Scope of Optimal Heating and Drying Process of Timber Heated by Microwave
Bibliographic record
Abstract
목재 건조의 용이성 및 품질확보, 2차 보존처리를 위한 기초자료 확보를 위해 초고주파를 이용한 캐나다산 직수입 미송의 가열 건조 특성은 다음과 같다. 초고주파 조사 후 목재 내부 온도변화 곡선을 분석한 결과 원주목은 3kW 30분 및 120분 가열 할 경우, 판목은 4kW 3분 및 9분 가열 시 안정적인 열분포와 소비 열량, 표면 함수분포 확보가 가능한 것으로 나타났다. 특히 초고주파 가열에 따른 표면 함수분포는 매우 균일한 Leveling이 이루어진 것으로 나타났으며, 함수 감소율도 중량대비 30% 이상인 것으로 나타나 건조효율이 우수한 것으로 나타났다. 또한 소비 열량 분석 결과에 따라 2차 액상 보존처리를 위한 침지 시 대량의 보존처리제 침투가 가능할 것으로 사료되므로, 초고주파 가열 목재는 빠른 함수감소 및 건조 특성과 많은 열량의 소비가 가능하여 산업재로의 사용 및 2차 응용제품 개발에 우수한 특성이 있는 것으로 사료된다. Characteristics of heating and drying of Canadian-imported Douglas-fir timber using microwave in order to secure convenience and quality of drying timber and to get basic data for secondary conservation treatment are as follows: As the results of analysis of change curves of internal temperature of the timber after irradiating it, round timber and board timber showed stable heat distribution, heat consumption and surface water-content in heating with 30kW for 30 minutes and 120 minutes and with 40kW for 3 minutes and 9 minutes, respectively. Especially, surface water-content distribution according to heating by microwave showed very even leveling and water-content decrease rate was also more 30% compared to its weight showing excellent dry efficiency. And in case of soaking timbers into liquid chemicals for secondary conservation according to the analysis results of heat consumption, it is expected that large amount of preservative will be permeated. Therefore, timber heated by microwave is remarkably available for using industrial materials and developing secondary utilized products with its fast water-content decrease and dryness and higher heat consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".