A Machine Learning Task Selection Method for Radar Resource Management (Poster)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A radar task selection method is proposed through a machine learning approach in order to improve the scheduling performance. The method initially sorts the tasks according to the importance determined by their dwell times and priorities. More important tasks are selected first until the time window for the task execution is full. A set of reward value is defined based on the initial order of the tasks' importance, then the order of the tasks' importance is changed iteratively according to an award-punishment policy in a reinforcement learning process. Finally, the best group of selected tasks is passed to the earliest start time (EST) algorithm for scheduling. By doing so, the performance of the task scheduling is significantly enhanced. The cost of the schedule is about 2.1 to 5.6 times less, under different overloading situations, than the EST. The proposed method is also very time efficient. A full cycle that including the task selection and scheduling only takes less than 15 ms, thus it is practical.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it