Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton
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

 
 
 Nowadays, determining faults (or critical situations) in non- stationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may re- solve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple critical situations). The main advantage of fuzzy finite state automaton is that their fuzziness allows them to handle imprecise and uncertain data, which is inherent to real-world phenomena, in the form of fuzzy states and transitions. Thus, most of approaches proposed for fault diagnosis of discrete event systems require a complete and accurate model of the system to be diagnosed. However, in non-stationary environment it is hard or impossible to obtain the complete model of the system. The focus of this work is to propose an evolving fuzzy discrete event system whose an activate degree is associated to each active state and to develop a fuzzy learning diagnosis for incomplete model. Our approach use the fuzzy set of output events of the model as input events of the diagnoser and the output of a fuzzy system should be defuzzified in an appropriate way to be usable by the environment.
 
 
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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 it