Benefits of Electronically Controlled Active Electromechanical Suspension Systems (EMS) for Mast Mounted Sensor Packages on Large Off-Road Vehicles
Bibliographic record
Abstract
Battlefield reconnaissance is an integral part of today's integrated battlefield management system. Current reconnaissance technology typically requires land based vehicle systems to observe while stationary or, at best, significantly limits travel speeds while collecting data. By combining current Canadian Light Armored Vehicle based reconnaissance systems with the Center for Electromechanics (CEM) electronically controlled active Electromechanical Suspension System (EMS), opportunities exist to substantially increase cross-country speeds at which useful reconnaissance data may be collected. This report documents a study performed by The University of Texas Center for Electromechanics with funding from L3-ES to use existing modeling and simulation tools to explore potential benefits provided by EMS for reconnaissance on the move. In both peak performance metrics and PSD analyses, the Fully Active Electronic Suspension systems proved their superior performance over Hybrid Active systems, Semi Active systems, and Passive systems. Peak body response measurements, accelerations, forces, and torques were consistently lower over aggressive off-road terrain with typical improvements being 80 to 90% lower than the stock passive vehicle. PSD and transfer function analyses of the payload accelerations revealed orders of magnitude improvement. Given these results it may be possible for reconnaissance operations to be carried out over aggressive cross country terrains at speeds of 10-15 MPH vs. conventional stationary operation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".