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
Internet-based teleoperation employs robots and internet a two breakthrough technologies to manipulate robots from distance for different applications. Variable and unknown time delay dynamics of internet is the main obstacle for realtime teleoperation via internet. In this paper the internet delay dynamics and its characteristics have been studied based on the measurement in different nodes. Then a black-box model for end-to-end internet delay dynamics has been developed using system identification and Auto-Regressive eXogeneous (ARX) model. Our experimental studies show a regular periodic behaviour in long-term intervals of internet delay variation and also confirm the accuracy and reliability of our theoretical and modelling derivations. This paper also introduces a novel multivariable control method for real-time telerobotic operations via Internet. Random communications delay of the Internet can cause instability in realtime closed-loop telerobotic systems. When a single identification model is used, it will have to adapt itself to the operating condition before an appropriate control mechanism can be applied. Slow adaptation may result in a large transient error. As an alternative, we propose to use a Multiple Model framework. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. We propose the use of Multi-Model Adaptive Control Theory and Multivariable Wave prediction method to capture the concurrency and complexity of Internet-based teleoperation. The results confirm the efficiency of the proposed technique in dealing with constant and variable delay dynamics of internet.
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 | 0.000 | 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 it