Matrix d-tree method and its application for symbolic analysis of linear parametric circuits in frequency domain
Bibliographic record
Abstract
In this paper, the time of solving such SSLAR was reduced by using one of subcircuit methods, namely, topological d-tree method. The existing d-tree method is used for circuits with constant parameters; therefore, this paper proposes its modification under the name Matrix d-tree method that is extended to circuits with variable parameters. This involves the use of the notion of parametric matrix model y = 1/r, g = 1/L, and C of variables and constant elements of parametric circuit. The d-tree method, both ordinary and matrix, provide a near-optimal taking out of similar terms in formed expressions. This result in a significant reduction of time required for their formation, decrease of the memory size required and the high operation speed of symbolic d-tree method as a whole. This leads to a significant extension of circuits admissible for analysis in terms of their complexity. The analysis of simulation of parametric ladder circuits presented in this paper has shown a significant increase of admissible complexity of circuits using the matrix d-tree method as compared with the use of standard tools of MATLAB. This fact makes it possible to materially extend the application scope of FS-method in problems of statistical investigations or optimization of electronic devices that are simulated by linear parametric circuits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".