Smart Parking Guidance Using Optimal Cost Function
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
The industrialization of the world, increase in population and mismanagement of the available parking space has resulted in parking problems. There is a need for an intelligent and reliable system which can be used for searching the unoccupied parking facility, to reduce the cost of leasing people and for better use of resources for car-park owners. This paper introduces an algorithm to increase the efficiency of the current smart-parking system. The main objective of this algorithm is helping users automatically to find an unoccupied parking lot with least cost based on a new performance metrics to calculate the parking cost. Considering the distance between the User and Parking, Distance between Parking and services area, Percent of free spaces in each car park and Cost of parking for a time t. Matlab software was used to compute the cost function and to save an optimal parking space upon a request by the user. The experimental results show that the proposed cost function helps improve the probability of optimal parking with least cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| 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