Optimizing the partitioning of tandem AGV systems using genetic and memetic algorithms
Bibliographic record
Abstract
An integrated circuit was designed to access and read a prototype chemical sensor affay.The addressing was accomplished using row and column decoders with master- slave D flip flops.The analog output circuit, containing logarithmic trans-impedance amplifiers, source follower circuits and transistor switches, converted the logarithmic sub-threshold sensor current to a voltage signal, and read out the voitage from the sensor.To demonstrate the feasibility of this approach, the circuits were integrated and fabricated with a 2x2 "pseudo-sensor" array on a chip using CMOS technology.Functional testing of the fabricated design verified that the integrated circuit accessed and read each sensor successfully.The experimental Vou,-I."nro,curves from a single sensor.confirmed the expected logarithmic relationship between current and output voltage from the sensor.A cross talk experiment demonstrated that the row and column decoders in the digital circuit efficiently routed digital signals to their respective rows and columns.Given the feasibility of the design has been verified, this type of circuit could be used to realize a truer "electronic nose" whete a much large float-gate, FET sensor array could be used.providing me this opportunity to engage in this ploject.I truly appreciate his invaluable suppofi, patient guidance and thesis revisions.His continuous support and encouragement has made this project possible.I have greatly enjoyed learning
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".