Enhancement of CN Tower lightning current derivative signals using a modified power spectral subtraction method
Bibliographic record
Abstract
Lightning current measurements are possible using instrumental tall structures or rocket-triggered lightning. The CN Tower has been a source of lightning current data for the past 15 years. A major portion of research on the natural lightning is focused on developing lightning protection systems, and in order to do so, an accurate knowledge of the characteristics of lightning, including the return-stroke current, is required. The CN Tower is a transmission tower and it is expected that the recorded lightning current signals be corrupted with different kinds of noise. This makes it difficult to extract the return-stroke current waveform parameters (peak, 10-90% rise-time to peak, maximum steepness, pulse width etc.) from the measured waveforms. In this project, an over-subtraction and residual noise reduction based power spectral subtraction method has been developed in order to de-noise the lighting return-stroke current derivative signals measured at the CN Tower. In order to evaluate the proposed de-noising technique, the derivative of Heidler function is used to model the measured return-stroke current derivative signal. The measured current derivative signal is simulated using the Heidler derivative model after artificially corrupting it with noise signals measured at the CN Tower in the absence of lightning. A modified spectral substraction method (MSS) is proposed and applied to the de- noise the simulated current derivative signal and the resultant waveform is compared with the Heidler derivative model, which enabled accurate evaluation of the proposed method. The result of the evaluation show a substantial improvement in the signal peak-to-noisepeak ratio(SPNPR) of up to 32 dB depending on the level of vthe noise signal, which is added to the Heidler derivative function. Furthermore, 95.7%-98.5% recovery of the peak of the original Heidler derivative function was obtained. For further evaluation of the new MSS method, the conventional spectral subtraction (SS) method is applied for de-noising the same simulated current derivative signals, which produced a substantially lower SPNPR of up to 16 dB with a peak recovery of 93.3%- 97.5% of the original Heidler derivative model. The poposed method is successfully used to substantially remove the noise from the lightning current derivative signals measured at the CN Tower.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".