Development of Control Strategies and Testing Procedures for Suspension Seats Used in High Speed Craft
Bibliographic record
Abstract
The objective of this work was to develop control strategies and testing procedures for the Slam Impact Seat Test Rig (SISTR). The SISTR was developed to more accurately test suspension seats for use in high speed craft (HSC). In general, HSC are defined as vessels which can achieve speeds higher than 30 knots, and are commonly used for search and rescue, law enforcement, and military operations. HSC are subject to extreme repeated shock and vibration loading and therefore so are their occupants. Therefore it is vital that the testing machines used to evaluate these seats can acurately reproduce the motion that HSCs are subjected to at sea. Development of control strategies involved developing and evaluating different types of motion control in order to accurately reproduce the desired motions. Once this was done, the user interface of the SISTR was developed to efficiently test different motions. A test procedure was then developed, which consisted of several types of impacts and other motions to represent what HSC experience at sea, as well as several benchmarking motions to demonstrate the capabilities of the SISTR. Compared to a conventional drop tower, the SISTR was able to more accurately reproduce HSC motion because it could directly control the position of the seat. Four different seats were tested using the SISTR, and the test results were processed to extract the overall damped natural frequencies and damping ratios of the seats.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".