Fusion of heterogeneous industrial data using polygon generation & deep learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analysis of industrial data imposes several challenges. These data are acquired from heterogeneous sources such as sensors, cameras, IoT, etc, and are stored in different structures and formats with different sampling frequencies. They are also stored in isolated silos in different locations which hinders their exploitation. Therefore, there is a clear need to integrate these disconnected data silos at different processing levels and make them clean, easily accessible, and fully exploitable. This paper proposes a data fusion method that merges heterogeneous sources of data at raw, information, and decision levels using polygon generation and deep learning (DL) techniques. An innovative polygon generation technique is proposed to preprocess each data source and convert it into powerful representations that capture all possible relationships in the data, thus extracting the maximum knowledge and achieving better prediction accuracy of the corresponding DL method. The proposed method is targeting challenging data modeling problems found in industrial processes. It is validated successfully using a case study in the realm of process system engineering. The results obtained demonstrate that the proposed fusion method is more accurate, with a minimum of 20% improvement, compared to other methods previously used in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it