Investigation on Circumstance Art Design Method Based on Edge Computing and Interactive Virtual Simulation
Bibliographic record
Abstract
In the new era, virtual simulation technology has made great contributions to education, medical treatment, entertainment, etc. With the development of the economy, the progress of society and the improvement of people's living standards, simulation technology has begun to affect the lifestyle and entertainment of modern people. It is also a new change for today's art and design work. In previous design work, designers often decide the design content by observing models and field trips. This is very one-sided, and the cost of time and manpower is also high. Therefore, the Virtual Reality (VR) technology that breaks through the traditional means of expression, static renderings and spectator models undoubtedly brings new opportunities to the reform of previous design work. However, the society is constantly developing, and the current production and life of virtual reality technology has put forward higher requirements. The virtual simulation technology based on cloud computing server relies heavily on the cloud data center, and the privacy and security cannot be guaranteed. Data redundancy, long computing time and high deployment costs have been criticized, and the current economic production requirements and life needs incompatible. Based on the above reasons, it can be seen that it is necessary to apply Edge Computing (EC) and Interactive Virtual Simulation (IVS) technology to circumstance art design. Compared with the traditional virtual technology based on cloud computing server, EC and IVS technology have huge advantages. EC and IVS technology adopt distributed computing, and are able to analyze, process and organize useful information in the first time. Data processing time is short, and EC only transmits and processes useful information. The operation efficiency was higher than that of cloud computing, reaching 33.2%. The cost was less than that of cloud computing virtual technology deployment, with an average of 38.9%. Privacy and security were relatively guaranteed. By comparing the three design methods mentioned above, through the relevant experimental data and practical experience, it could intuitively show the incomparable advantages of the design method based on edge computing and interactive virtual simulation technology and the inevitable trend of its wide application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".