A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
Bibliographic record
Abstract
Abstract In the field of RFID, the reading performance of tags is an important performance indicator for measuring tag. Related studies have shown that the tags’ geometrical distribution has an important influence on the tags’ reading performance. In order to optimize the tags’ geometrical distribution and improve the tags’ reading performance, this paper proposes a tag distribution optimization method based on multi‐level wavelet‐CNN (MWCNN) and extreme learning machine (ELM). First, this paper designs a tag distribution optimization system based on stereo‐vision. Second, the stereo‐cameras are used to capture the images of the tags. Aiming at the degradation phenomenon in the acquired images, MWCNN is used to recover the degraded tag images. On the basis of the image restoration, the template matching method is used to obtain the 3D coordinates of the tags. Then, ELM is used to model and predict the nonlinear relationship between 3D coordinates of the tags and the corresponding reading distance. The results show that the average prediction relative error is 0.56% and the time cost is 2.0 s. The average prediction relative error of ELM is smaller than GA‐BP and PSO‐BP. The time cost of ELM is smaller than the wavelet neural network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".