Accurate and Efficient Digital Twin Construction Using Concurrent End-to-End Synchronization and Multi-Attribute Data Resampling
Bibliographic record
Abstract
Accurate and efficient digital twin construction through real-time multi-attribute sensing and remote concurrent data analysis is essential in supporting complex connected industrial applications. Given the unsynchronized nature and heterogeneous sampling rates of distributed sensing processes, the varying time misalignment among different attributes will inevitably deteriorate the remote correlation analysis and digital twin construction. Furthermore, application-agnostic digital twin construction approaches could potentially involve high communication and computation overhead for comprehensive digital twin construction. In this article, a concurrent end-to-end time synchronization and multi-attribute data resampling scheme is proposed to enable accurate and efficient digital twin construction at the remote end. Specifically, digital clocks are concurrently established at the remote end, with each of them associated with a sampling rate of a unique sensing attribute. To tackle the temporal misalignment among multiple sensing attributes, raw data are accurately resampled according to the same reference frequency, with attribute-specific synchronized digital clocks providing cohesively aligned time information. An edge-centric platform is established to efficiently guide the multidimensional data processing during digital twin construction. Simulation results demonstrate that the proposed scheme can achieve more accurate and efficient digital twin construction than existing modeling methods. In the end, the digital twin-driven predictive maintenance is presented as a case study, aiming at illustrating the potential applications and benefits expected of the proposed scheme in industrial environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".