Digital Twin for Machine-Learning-based Vehicle CO<sub>2</sub> Emissions Concentration Prediction in Embedded System
Bibliographic record
Abstract
In this paper, we describe the design, implementation, and installation of a digital twin version of a physical CO2monitoring system with the aim of democratizing access to affordable CO2emission measuring and enabling the creation of effective pollutant reduction strategies. The presented digital twin acts as a replacement that enables the measuring of CO2emissions without the use of a physical sensor. The exhibited work is specifically designed to be installed on a low-powered Micro Controller Unit (MCU), enabling its accessibility to a broader base of users. To this end, an optimized Artificial Neural Network (ANN) model was trained to be capable of predicting CO2emission concentrations with 87.15% accuracy when performing on the MCU. The ANN model is the result of a compound optimization technique that enhances the speed and accuracy of the model while reducing its computational complexity. The results outline that the implementation of the digital twin is 86.4% less expensive than its physical CO2counterpart, whilst still providing highly accurate and reliable data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".