Real-time evaluation of power quality using FPGA based measurement system
Why this work is in the frame
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Bibliographic record
Abstract
Real-time evaluation of power quality is a desired feature in research and industrial projects, especially when embedded systems are employed and/or studied. Fast Fourier Transforms FFT is commonly used to evaluate the harmonic content of electric signals. Artificial Neural Networks (ANN) are also employed for harmonics estimation with short processing time and low implementation complexity. Commercial power quality measurement systems are available and offer good performance, communication and storage capabilities, and other special features, however in most of them the real-time information is not available or it is offered with important communication delays. This paper presents the implementation of a measurement system using Xilinx FPGA target and the Adaptive Linear Neuron (ADALINE) algorithm for real-time evaluation of power quality. Experimental results show that the implemented system can be employed for power quality monitoring and embedded control applications.
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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 | 0.005 | 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 it