Development of active suspension system for a quarter car model using optical incremental encoder and ultrasonic sensors
Bibliographic record
Abstract
Nowadays in the industrial world, quality factors are the reasons of growth and survival of an automotive unit. Suspension system as an effective part of vehicle can achieve two factors, safety and convenience. It plays an important role in the quality of the car. Therefore, it is necessary to carry out an analysis and evaluation of how the suspension system responds in different vehicles under various conditions whilst improving vehicle quality. Considering the power units and automotive vehicle production capacity in developing countries, the need of a vehicle with features such as durability and accuracy as suitable standard for passenger vehicles was felt. This paper describes the development of a new system able to predict and scan road profile and its condition. Vehicle equipped with this predictor technology use measurement sensors such as Ultrasonic and Optical Incremental Encoder. They can provide enough information about road condition and vehicle position by measuring distance or the angle variation of body and vehicle’s chassis in order to present flexible suspension in different conditions such as high speed, rough road, bumps and emergency situations. The quarter car model with active mechanical suspension can illustrate the mentioned characteristics. Key words: Vehicle suspension system, quarter car model, ultrasonic, optical incremental encoder.
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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 | 0.001 | 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 | 0.000 | 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".