Knock Detection and Control in an HCCI Engine Using DWT
Bibliographic record
Abstract
The novel application of the Discrete Wavelet Transform (DWT) in a real time controller is used to detect and subsequently control knock in a Homogeneous Charge Compression Ignition (HCCI) engine. Classical Fourier techniques for knock detection are discussed and compared to Wavelet Transforms. The Discrete Wavelet Transform filter bank is chosen as the best method for knock detection due to its good time-resolution and low computational requirements. The DWT method is compared with the root mean squared value of the pressure trace as the benchmark method for determining knock and the two methods are linearly correlated. Using the DWT method for knock detection and modulating fuel octane, both a Proportional Integral (PI) and PI with Feed-forward control are implemented. Both of these methods reduce knock intensity for a step increase in engine load. The combination of Feed-forward with PI feedback is found to be slightly more effective than just PI feedback control.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".