An Efficient Energy Transmission Combination Approach for Charging Smart Wearable Devices
Bibliographic record
Abstract
With the rise of smart wearables and growing demand for longer battery life, wireless charging has become essential, especially when wired options are unavailable. Current wireless energy charging methods for wearable devices are mainly based on radio frequency wireless charging technology. However, due to the low energy transmission efficiency of individual wireless charging devices, multiple wireless transmission devices are combined to meet the energy demands of wearable devices. Consequently, efficiently combining multiple wireless energy transmission devices to supply power to a wearable device has emerged as a critical issue. To address this challenge, we propose an approach for optimizing the combination of wireless energy charging devices based on a deep reinforcement learning-based ant colony optimization algorithm. We leverage deep reinforcement learning to automatically adjust the heuristic parameters and strategies of the ant colony algorithm, thereby enhancing its search capability. This effectively solves the energy transmission combination optimization problem. Experimental results demonstrate that the energy combination plans of the proposed algorithm are more efficient than those generated by baseline and state-of-the-art algorithms. Additionally, we explore the combination scheme with minimal energy transmission loss and demonstrate that our approach consistently yields solutions with lower energy loss compared to other algorithms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".