Accurate Passive Indoor RFID Alignment System for Service Station
Bibliographic record
Abstract
Nowadays, motorized vehicles are essential in our daily lives, therefore fuel supply services should be efficient and easily accessible. The fuel supply may encounter some difficulties, such as queues, non-continuous availability of fuel, and fraud. The problems of long waiting queues and non-continuous availability of fuel can be solved by going to the next fuel station. However, fraud is a more serious issue that is more difficult to solve. We intend to develop an intelligent system for fuel supply management to solve this problem. For safety reasons, we must avoid the risk of causing a spark in the fueling environment. An electric system close to the pump, the hose, or the vehicle fuel tank may be a risk. We opted for the RFID (Radio Frequency IDentification) technology and the use of passive tags, since semi-passive or active tags involve a battery, on one hand, and are significantly more expensive, on the other hand. A motorized vehicle is identified by a passive RFID tag, and two other passive RFID tags are used for the fueling of all cars from the given fuel pump. Our work consists of the design, by research, of the required system and focuses on the optimization of the topology of antennas and tags so that frauds are prevented. The technique is based on the alignment of tags.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".