Inertial measurement unit (IMU) testing procedure
Bibliographic record
Abstract
Inertial Measurement Units (IMUs) are electronic devices that return a craft’s angular velocities and translational accelerations. They are used in many industries, such as underwater and aviation robotics. There is a large variation in IMU cost, and when considering the dependability of different IMUs, generally their manufacturers provide completely different metrics to quantify their performance. Because of this, it is very difficult to decide which sensor has a more reasonable cost-to-performance ratio for any particular project.To solve this dilemma, research and experimentation at IOT began. First a specialized apparatus was created to mount all IMUs to the same rigid body. MATLABÔ applications were then written to simultaneously record data from all of the IMUs, while they were experiencing motion. Unfortunately a series of issues continued to occur, and after many trials, it was decided that a solution other than these MATLAB applications had to be designed.The Data Logger application is data acquisition software written in Java and is designed to be as lightweight and robust as possible. This application was created as a solution to the issues experienced with the formerly mentioned MATLAB applications. Once Data Logger was finished, experimentation was much more simple and the testing could begin again.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".