Design of single phase inverter using microcontroller assisted by data processing applications software
Bibliographic record
Abstract
Inverter is widely used for industrial, office, and residential purposes.Inverter supports the development of alternative energy such as solar cells, wind turbines and fuel cells by converting dc voltage to ac voltage.Inverter has been made with a variety of hardware and software combinations, such as the use of pure analog circuit and various types of microcontroller as controller.When using pure analog circuit, modification would be difficult because it will change the entire hardware components.In inverter with microcontroller based design (with software), calculations to generate AC modulation is done in the microcontroller.This increases programming complexity and amount of coding downloaded to the microcontroller chip (capacity flash memory in the microcontroller is limited).This paper discusses the design of a single phase inverter using unipolar modulation of sine wave and triangular wave, which is done outside the microcontroller using data processing software application (Microsoft Excel), result shows that complexity programming was reduce and resolution sampling data is very influence to THD.Resolution sampling must taking ½ degree to get best THD (15.8%).
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".