Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment
Bibliographic record
Abstract
Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used to create the reference data in the format of Comma Separated Values (CSV). This reference data is first analyzed for quality issues and then used for navigation purposes. Further for the localization Sugeno Fuzzy Model is used as a fuzzy inference system for the initial localization and subsequent mapping of the location. The proposed approach is validated using an android application on seven predefined routes. According to the performed result analysis, the proposed fuzzy logic-based approach is able to provide location identification with 98.9 percent accuracy with a root mean square error value of 3 percent.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".