Satisfying Statistical Constraints in Preparing Edited Variable Amplitude Loading History Using Genetic Algorithm
Bibliographic record
Abstract
A major concern that surfaces when performing the segment-based fatigue data editing technique is to certify that the values of two global statistics (root mean square and kurtosis) of the edited load history are within an acceptance interval whilst maximizing the data reduction rate and minimizing the loss in damage. The root mean square (rms) quantifies an overall energy underlying the history whilst kurtosis is important to identify impulsive character. In this paper, the stochastic Genetic Algorithm (GA) is employed as a post processing tool that helps the edited history satisfy the statistical requirements with minimum cost i.e. small decrement in the initial reduction rate. Consider the initial version of edited history being composed of high fatigue damage segments resulted from the non-overlapping segmentation method. In a case that the history does not comply with the statistical requirements, then importing a subset of low segments into the present edited history might reverse the outcome. Thus, the GA aims to search for the smallest subset that turns the history into fulfilling the rms and kurtosis needs without affecting the reduction rate too much. Experimental results show the capability of the proposed method in making the edited history fit the statistical constraints without imposing harm on the overall fatigue damage value.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".