Real-Time Implementation of Asynchronous Machine using LabVIEW RTX and FPGA Module
Bibliographic record
Abstract
This paper describes the implementation of squirrel cage induction motor using high level graphical language in LabViewsoftware. The mathematical models of the three-phase induction motor are implemented in LabViewblock diagram pages. These models are in form of differential equations and Runge-Kutta 4th order method is implemented to solve the problem. LabViewFpga module alongside with Xilinx 10.1 compiler will generate the Bitfiles, and then Synthesize, Route and place the logic gates to a FPGA chip. NI PCI-7831R is programmed to communicate with real system as a data acquisition card. This communication is implemented in real time environment (RTX) through Ardence RTX as a multithread and multitasking software. The presented real-time platform provides a versatile and flexible simulation tool for investigating dynamic behaviors of induction machine in different cases and can be extended to many other equipment and subsystems of electrical power systems. To validate implemented squirrel cage induction motor model and also its behavior in LabViewsoftware, human machine interface (HMI) is developed in LabViewfront page. A study case is simulated in LabViewreal-time system and MATLAB/Simulink, as two independent software. Results show that motor characteristics (i.e. speed, torque, and etc.) are in the close correspondence compared to MATLAB outputs and consequently LabVIEW model is verified.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.018 | 0.005 |
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".