Complex Integration of Aerodynamic Micro-Foam Generators into Specialized Technological Devices with Artificial Intelligence and Artificial Neural Networks for System Control
Bibliographic record
Abstract
The system of aerodynamic foam generation is covered in this article as well as its structure and main technological and structural characteristics. The author describes technological and industrial processes related to production of thin filmed micro assemblies from which logically follows the expediency of using micro foam for solving specified objectives, its main advantages as well as reasoning behind choosing the aerodynamic principle for foam generation. Besides the principles of system operation, the author also considered different options for its application in industrial settings. Special attention is focused on the application at lines of photolithographic masking and galvanic coating on the boards of thin filmed micro assemblies, but the author also considers a case for usage as a fuel mixture which leads to reduction of fuel consumption and simplification of the construction of combustion chamber sealing or cylinders of the diesel engine. Author considers in detail the main structural components of the construction of the device for aerodynamic micro foam generation as well as the properties and characteristics of the obtained micro foal primarily due to aerodynamic effect. Thorough description is given to the principle diagram and principles of assembly operation for using the aerodynamic foam generator for various industrial technological processes. Comparative analysis is conducted for the suggested technical object and known technical objects that were discovered during patent search. As a result, the list of properties for significant novelty is elicited and outlined. The described system allows usage of artificial intelligence and machine learning for system control. Analyses of the suggested technology was performed in accordance with the methods and criteria of Theory of Inventive Problem Solving and Algorithms of Inventive Problem Solving.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".