Bibliographic record
Abstract
This paper describes an application of LabVIEW to system control which includes data acquisition, data processing and the display of data.The application described in this paper emphasizes the hardware and, perhaps to a greater extent, the software used to control a physical process.The use of the computer in data processing and control applications is a trend that one sees in today's industrial environment.This application is one of many that is offered to the students in the Industrial Controls laboratory at DeVry, in order to provide them with hands-on experience that they are likely to experience on the job.Virtual Instrumentation is a current technology that is making a significant impact in today's industry, education and research.DeVry Institute selected LabVIEW as an good representative of this technology and is using LabVIEW in its curriculum at all DeVry campuses in the United States and Canada.This article is a result of a research project for LabVIEW implementation into the Industrial Controls course.LabVIEW is also used in the communication and physics courses.LabVIEW is one of many skills that the student will need as he enters today's highly competitive job market.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".