Matrix D-Tree Method and Its Application for Symbolic Analysis of Linear Periodically Time-Variable Circuits in Frequency Domain
Bibliographic record
Abstract
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 periodically time-variable 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 periodically time-variable 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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".