Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Active power-line filtering is conventionally performed by injecting equal-but-opposite of the distortion into the line. The power converter used for this purpose is rated based on the magnitude of the distortion current and operated at the switching frequency dictated by the desired filter bandwidth. Fast switching at high power, even if technically possible, causes high switching losses. In this paper, a new modular approach to active harmonic filtering is proposed. The method utilizes two linear adaptive neurons (ADALINEs) to process the signals obtained from the line. The first ADALINE (the current ADALINE) extracts the harmonic components of the distorted line current signal and the second ADALINE (the voltage ADALINE) estimates the fundamental component of the line voltage signal. The outputs of both ADALINEs are used to construct the modulating signals of a number of current-source inverter (CSI) modules, each dedicated to eliminate a specific harmonic. The power rating of the modules will decrease and their switching frequency will increase as the order of the harmonic to be filtered is increased. The overall switching losses are minimized due to the selected harmonic elimination and balanced "power rating"-"switching frequency" product. Power losses are also reduced by adjusting the Idc, in each CSI module according to the present magnitudes of the individual harmonics to be filtered. Speed and accuracy of ADALINE; self-synchronizing harmonic tracking; optimum Idc value and minimal converter losses; high reliability, flexibility, and speed; and low dc energy requirement of the CSI result in superb performance of the proposed active conditioner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it