Comparison testing of multiple inertial measurement units
Bibliographic record
Abstract
Inertial measurement units (IMUs) are used extensively at the NRC-IOT for ship model testing and research experiments. In many cases, selection of the correct IMU is key to the success of the experiment. Unmanned underwater vehicle (UUV) designers, such as Marine Robotics Inc., face a similar challenge of selecting an appropriate IMU for their specific application. As a result, the NRC-IOT has commissioned a study of the performance of several IMUs that will provide side-by-side comparison data to assist instrumentation engineers and designers in the IMU selection process. The study consists of four main parts: design of a test apparatus, development of a data acquisition system, simultaneous IMU testing, and data analysis. Each part of the study is addressed in a separate section of this report, however the main focus of this work is on the physical IMU tests performed between May and August 2011 at the NRC-IOT. The IMU tests feature side-by-side testing of a Crossbow, MicroStrain, MotionPak-II, PHINS, Watson, and Xsens IMU. The report documents the equipment, test configurations, and procedures used to perform the IMU tests and presents some initial test results along with discussion and conclusions. Complete test logs are also included in this report to assist with the data analysis phase of the study.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".