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Record W2996227322 · doi:10.32370/ia_2019_12_15

Complex Integration of Aerodynamic Micro-Foam Generators into Specialized Technological Devices with Artificial Intelligence and Artificial Neural Networks for System Control

2019· article· en· W2996227322 on OpenAlexvenueno aff
Victor Popov

Bibliographic record

VenueIntellectual Archive · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsAerodynamicsComputer scienceMechanical engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.038
GPT teacher head0.254
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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