Hopfield Neural Network-based Fault Location in Wireless and Optical Networks for Smart City IoT
Bibliographic record
Abstract
With the rapid evolution of smart city all over the world, the appealing services of IoT and big data analytics have prompted the design of more reliable assurance mechanism for network quality. It has been a crucial issue of network operation that once multiple links fail simultaneously, the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can't satisfy the requirement due to the expansion scale of wireless and optical networks and the growing demands of customers. In this paper, we propose an efficient multi-link faults location algorithm based on Hopfield Neural Network (HNN). We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by experiments that this method can achieve real-time faults location while ensuring positioning accuracy, which provides a good solution for smart city service assurance.
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".