NEURAL NETWORK-BASED PREDICTION OF CARDIOVASCULAR RESPONSE DUE TO THE GRAVITATIONAL EFFECTS
Bibliographic record
Abstract
It is well-documented in the literature that the orthostatic stress during flight maneuvers induces changes in pilots' cardiovascular system by imposing dramatic changes to the blood circulation process. Research reported for prediction of cardiovascular system dynamic response during G-transition is limited. As such, more models are needed to gain insight into the behavior of cardiovascular response during G-transition. Therefore, the objective of this paper is to develop two novel models based on: (1) artificial neural network (ANN) and (2) adaptive-network-based fuzzy inference system (ANFIS) to predict the variations of the blood pressure (BP) with respect to flight maneuver's parameters, i.e., dwell time, during G-transitions. The proposed models will be used to provide operational recommendations and pilot selection for any routinepredefined flight maneuver. The training data for ANN and ANFIS are based on experimental data sets collected from a man-rated electronic tilt table that applies gigahertz-acceleration transition from +0.861 Gz (head-up (HU)) to -0.767Gz (head-down (HD)) and back to +0.861 Gz (HU) using either pitch or roll rotation. A case study is presented on how the model is intended to be used in future to predict pilot's cardiovascular response and evaluate the pilot's qualification for a specific maneuver.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".