Research on the optimization method of computational model in the design and application of subsurface fluid stratified sampling device
Bibliographic record
Abstract
In order to improve the automation and intelligence level of underground fluid sampling, this paper proposes a kind of underground fluid automatic sampling device, and carries out the structural design of the sampling device, the control system design and the field experiment test.According to the action process and movement characteristics of the underground fluid stratified sampling device, the control system needs to use multiple electromagnetic control valves to control the switching of the oil circuit of different actuators respectively.In order to improve the control state and response speed of the stratified sampling device system, a fuzzy identification algorithm is chosen to identify the control model, and the MIMO robust generalized predictive controller is used as the robust adaptive controller of the system to realize the low-flow and low-disturbance acquisition of underground fluids at the same monitoring point and at different depths.In the field sampling, the average values of DO at sampling depths of 1m, 2m, 3m, 4m, and 5m for manual sampling, vertical sampling, and fuzzy adaptive device sampling under the 1-2 sampling plumb line were 7.98mg/L, 7.86mg/L, 8.25mg/L, 7.83mg/L, and 7.77mg/L, respectively.The deviation of dissolved oxygen content at the same sampling point in the three ways is small and the trend of change is consistent at different depths.It shows that the fuzzy adaptive stratified sampling device system designed in this paper can be applied to the sampling of subsurface fluids with dissolved oxygen as the detection target.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".