A self-sensing approach to the energy-saving operation of electromagnetic locking devices
Bibliographic record
Abstract
This paper reports an energy-efficient operation of electromagnetic (EM) locking devices using the inductive self-sensing technique, i.e. employing the existing actuation coil as a sensing medium. EM locking devices such as EM locks and locking solenoids have been increasingly used as new security devices in industrial and commercial buildings due to their fully electronic operation. One of the drawbacks of such EM devices is that a significant amount of energy is often wasted through their continual use, especially in applications where the maximum holding power is seldom needed. The main idea of this paper is to operate an EM locking device in such a way that its full power is supplied only when needed. More specifically, we propose running the existing coil as an inductive sensor using significantly less power during normal operation. Any imminent attempt to disengage an EM lock can be instantly detected by the self-sensing coil and its full locking power can then be engaged to prevent it from unlocking. This paper presents a detailed electromagnetic analysis for the self-sensing functionality of the existing EM lock. Besides, a stochastic detection method called the generalized likelihood ratio test (GLRT) has been implemented to improve the energy-saving operation of the EM device. Proposed strategies are experimentally verified using a commercial EM lock, which demonstrated that more than 75% of the original power can be saved by the proposed method while keeping the same locking performance.
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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".