Bibliographic record
Abstract
Concurrent engineering is a promising paradigm for the analysis and synthesis of complex, multidisciplinary systems, such as robot manipulators. It brings synergy as a direct consequence of utilizing design knowledge from all participating disciplines, while interacting with each other, and offering equal opportunities to them to contribute to each state of design simultaneously. The advantage, however, does not come at no cost; one must deal with highly-complicated mechatronic system models, and handle optimizations with a large set of multidisciplinary objective and constraint functions and a great number of design variables. The compromise seems to be either to simplify the system model to reduce dimensions of the design space, or to give up the transparency of the design process and appeal to parallel computing algorithms. This chapter discussed an alternative methodology that does not imply any of the above compromises. The new methodology makes the system model computations efficient without compromising design transparency, because it uses the physical system components in the simulation loop, next to the computational model of those modules that need to be designed. The robotic hardware-in-the-loop simulation platform enables the designer to take into account some complex phenomena that are difficult to model, yet execute the entire simulation in real-time. Using hardware components in concurrence with the computational model of the modules that are to be designed results in an effective platform for rapid design alterations. Moreover, the new methodology alleviates the optimization complexities of concurrent design, because it employs Linguistic Mechatronics that not only transforms the multi-objective constrained optimization problem into a single-objective unconstrained formulation, but also formalizes subjective notions and brings the linguistic aspects of communication into the design process.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".