Bibliographic record
Abstract
This thesis presents two types of FPCB (Flexible Printed Circuit Board) micromirrors, i.e., 1D (rotation about one axis) FPCB micromirror and 2D (rotation about two perpendicular axes) FPCB micromirror. Each type of micromirror is integrated with a commercial single-point LIDAR (TF03) to form a scanning 3D LIDAR, which can be used in autonomous guided vehicles (AGVs). Using the 3D scanning LIDAR, real-time 3D point cloud maps can be generated by collecting positional data and processing it on a computer. The first 3D LIDAR scanning system uses two 1D FPCB mirrors rotating about perpendicular axes to steer an infrared (IR) laser from the LIDAR (Class 1 laser) and collect the diffusion reflected light at the receiver. This system is capable of: 1) field of view (FOV) of 38°×24°; 2) 12.5 or 25 horizontal scanning lines, 3) refreshing rate of 2 or 4 frames per second (fps), 4) 0.2~20 meters detection distance, and 5) 1,250-5,000 points per frame. The second 3D LIDAR system uses a single 2D FPCB mirror to steer the LIDAR light in two axial directions where one axis is actuated with Lorentz forces and the other by electromagnetic solenoids. This second system can achieve: 1) FOV of 40°×24; 2) 15 or 30 horizontal scanning lines; 3) refreshing rate of 2.5 or 5 fps; 4) detecting distance of 0.2~20 meters; and 5) 1,000-4,000 points per frame.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".