Proposal of Coastal Flooding Scheme Using Smart Balloon Powered by Wind Turbine Generator
Bibliographic record
Abstract
Some coastal cities are sometimes exposed to floods, mainly caused by strong winds or earthquakes on the seafloor.This causes the water waves heading to the coastal cities to rise quickly, possibly destroying civilization.The proposed study will introduce an intelligent rubber balloon that automatically acts as a water repellent to absorb the momentum of water hammers from the sea.The proposed system has been energized by wind power energy.This enables the control of the bus voltage of the DC link.Sequential balloons could be arranged in such a matter to form a repel flood wall.Wind turbine generators could be used for charging the storage batteries.These batteries energize the smart control system and DC motors coupled with air pumps.These pumps are used to inflate the sequential air balloons.The theoretical models of the proposed system components have been simulated by MATLAB environment.Three DC motors are connected based on the master-salve mechanism, and the third is considered in standby mode.These motors are controlled by a model reference adaptive controller.The tracking speed between reference and measured speeds has been accomplished.Control of switching ON-OFF balloons using fuzzy logic control and classical control has been compared.After using several control scenarios for air pressure in balloons, it is observed that the best response is obtained using fuzzy logic control since it reduces the setting time and faster time response compared to the classical PID controller.Also, it was noticed that time response improved when using a PID controller instead of proportional or PD control scenarios, and the system dynamic response became acceptable.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".