Supervisor Karl-Erik Årzén at LTH
Bibliographic record
Abstract
Sponsoring organization Title and subtitle Hardware Simulation for Testing IEC 61131-3 (Hårdvarusimulering för testning av IEC 61131-3) Testing of control code written in IEC 61131-3 has long been a strenuous manual process. The focus in this master's thesis is on building a simulator of an Alfa Laval separator to enable testing of control code. In this particular case, the control code being tested is written and executed in a soft PLC called TwinCAT. The simulator for testing code is written in C#. When testing code, automated tests are preferred because it enables easy regression testing. For this purpose a testing tool using a customized script language has been developed. Testing with a simulator is also beneficial because errors can be found early in the development process, thus reducing the number of errors left to be found when testing on hardware. Comparison tests show that the simulator performs sufficiently well for testing of control code. Keywords Classification system and/or index terms (if any) Supplementary bibliographical information ISSN and key title
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.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.451 | 0.372 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".