Adaptive Policy Evaluation With Adjustable Step Sizes for Active Quarter-Vehicle Suspension Systems Under IoT Environment
Bibliographic record
Abstract
The rapid development of the Internet of Things (IoT), along with the widespread adoption of 5G and time-sensitive networking (TSN), has provided reliable communication support for the development of Internet of Vehicles technologies. As a critical component of intelligent vehicles, active suspension systems under IoT environment play a vital role in enhancing ride comfort and vehicle safety. However, failure to process data uploaded to the cloud promptly may lead to data accumulation, which can subsequently cause data loss, resource wastage, or system response delays. In this article, an adaptive step value iteration (ASVI) algorithm is designed for solving the optimal control problem of active quarter-vehicle suspension systems (AQVSSs), significantly improving the data analysis efficiency of the cloud layer. To prevent divergence caused by excessive policy evaluation step sizes under immature policies, this algorithm incorporates an adaptive step-size adjustment function that is upper bounded and monotonically nondecreasing. Based on convergence of the value function and stability criteria of control policies, an integrated ASVI (IASVI) algorithm is proposed, which avoids the need of admissible control policies and greatly improves learning efficiency. Feasibility and superiority of the IASVI algorithm are verified through a hardware-in-the-loop (HIL) simulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".