A Framework of CAD/CAE Integration System and its Implementation for Container Crane
Bibliographic record
Abstract
A framework of CAD/CAE integration system and its implementation for dockside container crane are proposed in this paper. First, the system framework based on web technology, software design pattern and service-oriented architecture (SOA) is introduced. Then, requirement input interfaces of Customer-Designer-Interaction (CDI) module are built based on ASP.NET multiple-layer Browser/Server (B/S) architecture, core design patterns and .NET WCF Services, and customers can provide specifications of the cranes to designers. Next, CAD and CAE modules are accomplished using multiple-layer architecture, and designers can parametrically create 3D models of the crane structures and conduct explicit dynamic Finite Element Analysis (FEA) on the designed crane structures. SOA based Design-Analysis-Integration (DAI) is developed to maintain consistence between CAD and CAE models by using .Net WCF Service. Last, system management functions such as user interaction, user account and file management are described. Since all the operations are conducted in Web and SOA context, customers and designers are able to participate in the design process at different geographical locations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".