Simulation of three dimensional elevator system using cell-DEVS formalism
Bibliographic record
Abstract
Complex physical systems have been studied for many years using different approaches. Skyscraper building heights are restricted by the design limitations of their elevator systems. Long cable elevator systems will cause stretch because of their own weight. A newer technology is electromagnetic elevators that are able to travel in three dimensions and have no limitation on their height. M&S (Modeling and Simulation) methodologies and tools provide means for cost-effective validity analysis for designing complex physical and mechanical systems. Cell-DEVS is a formal methodology for cell-divided models based on DEVS (Discrete Event System Specifications) formalism. In this work, a cellular simulation model is used to model a three dimensional elevator system in a tall building with huge occupied area. The model defines appropriate rules for cells to control the elevators moving in different directions, while applying certain regulations to their movement to avoid collisions. Path finding and collision avoidance strategies are used to simulate an applicable system. We present the elevator model specifications, simulation design and discuss different simulation scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".