Matrix d-tree method and its application for symbolic analysis of linear parametric circuits in frequency domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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