Fuzzy algorithm for intelligent wireless sensors with solar harvesting
Bibliographic record
Abstract
Wireless sensors are sophisticated embedded systems designed for collecting data on systems or processes of interest. In many cases, they are expected to operate in inaccessible locations, without user supervision. As a result, such monitoring systems need to operate autonomously and independently of external sources of energy. To achieve long-lived sustainability, monitoring systems often rely on energy extracted from the environment, e.g. through solar harvesting. Their design is a challenging problem with several conflicting goals and a number of design and implementation possibilities. For obvious reasons, these devices must be designed in an energy efficient way. As a result, they usually have low computational performance and cannot implement complicated control algorithms. At the same time, due to the requirements for autonomy and dependability, they must be endowed with certain degree of adaptability and fault tolerance - properties typically found in intelligent systems. In this contribution, we describe the design flow of an intelligent embedded control system for management of energy use in wireless monitoring systems. The paper also provides a simulation-based analysis of the control system performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".