FAULT EVALUATION OF UNMANNED AERIAL VEHICLES POWER SYSTEM WITH AN IMPROVED FUZZY GROUP DECISION-MAKING
Bibliographic record
Abstract
The applications of unmanned aerial vehicles (UAVs) in military and civilian service domains have exhibited unprecedented growth in the last decades.However, the sophisticated and expensive UAVs are susceptible to multiple faults, such as wear and tear, noise, or software-control failures.The mutual recognition of community opinion in fuzzy multiple attribute group decisionmaking (FMAGDM) is an efficient way to solve a complex system.However, whether it can be utilized to evaluate the fault of the power system of UAVs has not been examined yet.This paper studies the evaluation towards the fault of the power system of UAVs under interval-valued intuitionistic fuzzy (IVIF) numbers with an improved FMAGDM.A new algorithm is proposed to achieve a desirable consensus in group decision-making.In contrast to the commonly used method, the proposed method comprehensively evaluates the fault of the power system of UAVs and basically gives an optimal consensus decision-making without modifying many elements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".