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Record W7117246188 · doi:10.1299/jsmermd.2025.2p1-n04

Investigation of Time Delay Measurement Methods for Enhancing the Stability of Remote Operation Systems Between Japan and Thailand

2025· article· en· W7117246188 on OpenAlexaff
Taisei Mikami, Yongyut Pattanapong, Wishapas KAEWMORA, Warakon Jantapoon, Tawatchai Jitson, Takanori MIYOSHI

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2025
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsPrograms for Assessment of Technology in Health Research Institute
Fundersnot available
KeywordsTeleoperationStability (learning theory)Measure (data warehouse)Focus (optics)TeleroboticsControl theory (sociology)Simple (philosophy)

Abstract

fetched live from OpenAlex

The variability in time delay significantly impacts the stability of teleoperation systems. Typically, simple time-delay approximations focus on estimating the round-trip time. However, these methods often lack high resolution and fail to provide statistical insights. This paper proposes a precise time delay measurement system for teleoperation systems, employing synchronized clocks between two sites based on GNSS. The proposed approach is evaluated through real-time mobile robot teleoperation experiments conducted between Thailand and Japan. The experimental results demonstrate the ability to measure and record time delays with high resolution, achieving microsecond-level precision.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.277
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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