Neural Network Soft Sensors for Gasoline Engine Exhaust Emission Estimation
Bibliographic record
Abstract
Abstract Worldwide research and development programs aim to reduce harmful emissions from transportation vehicles. Soft sensors have shown great potentials to reduce cost and improve onboard diagnosis for vehicle emission control. In this work, two sets of soft sensors are proposed to predict the emissions and exhaust heat flux of a gasoline engine. Extensive steady-state measurement points over the entire engine operating conditions are collected for model training and validation, and the locally linear model tree learning method is adopted. The CO, NOx, hydrocarbon, exhaust temperature, and exhaust heat flux are estimated by the soft sensors under steady-state conditions. Training of CO, exhaust temperature, and exhaust heat flux models has achieved high model accuracy over the entire engine map. Local models are developed for NOx and HC emissions to improve model performance at different engine operating speed/load conditions, especially in the low emission zone. Model validation has shown correlation coefficients ranging 0.983 ∼ 0.999
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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".