Development and Application of Mechanical Design Engineering Database Based on Simulated Annealing Algorithm
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.
Bibliographic record
Abstract
In the process of mechanical engineering design, it is necessary to involve multiple links such as design, analysis, simulation, experiment, etc. There will be a large amount of data and information exchange between each link. The use of database technology can effectively improve the quality of data management, reduce data redundancy, and improve data sharing. Mechanical design engineering data is the basic and core data in the entire mechanical product design process. These engineering data have the characteristics of various types, complex relational structures, dynamic modification of patterns, and large amount of data. Traditional data models cannot fully meet the needs of engineering data description and management. In order to meet the needs of practical applications, people have proposed a variety of data models to meet the needs of different fields. These data models either extend the traditional relational model, or adopt the object-oriented model and other kinds of special databases, which show great power in the field of engineering application. The method used in this paper is the simulated annealing algorithm. Compared with the conventional optimization method, the simulated annealing algorithm has good convergence and strong adaptability, and is a good method to realize reactive power optimization.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it