An Adaptive Particle Filter Technique for System State Estimation and Prognosis
Bibliographic record
Abstract
System state estimation and prognostics are the key issues in dynamic system monitoring and management. Although the particle filter (PF) has been applied to model the nonlinear degradation feature of the system aging mechanism in several studies, it has two potential problems: the sample degeneracy and the impoverishment. To tackle these problems, a new adaptive particle filter (APF) technique is proposed in this article to enhance the performance of PFs. In the APF, a self-evaluation method is suggested to track the posterior distribution and detect the low-weight particles (sample degeneracy). A new adaptive weight adjustment approach is proposed to adaptively explore the posterior space, process those low-weight particles, and tackle the sample degeneracy. The effectiveness of the proposed APF technique is validated by simulation tests using several model conditions. It is also implemented for battery-health monitoring and prognosis. Test results show that the proposed APF technology can effectively capture and track the system's dynamic characteristics.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".