Distributed state estimation for large-scale nonlinear systems: A reduced order particle filter implementation
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Bibliographic record
Abstract
Motivated by state estimation problems in power distribution networks (PDN), the paper proposes a fusion based, reduced order, distributed implementation of the particle filter (FR/DPF) for large scale, nonlinear dynamical systems with localized sensor observations. Direct application of the centralized particle filter is computationally challenging due to the high dimensions of the state-space dynamics. Based on partitioning the overall system into N localized but mathematically coupled subsystems, the near-optimal FR/DPF provides computational savings of a factor of N over the centralized particle filter implementation. By introducing distributed state and observation fusion steps, the proposed FR/DPF does not require a fusion centre and maintains consistency between the local sub-systems. In our Monte Carlo simulations of a simplified PDN, the performance of the FR/DPF is consistently close to that of the centralized implementation.
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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 | 0.001 |
| 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