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Record W3108440450 · doi:10.1299/jsmermd.2020.2p1-g01

Robotization for IoT Cloud Observation System using OpenRTM

2020· article· en· W3108440450 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2020
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsCooke Aquaculture (Canada)
Fundersnot available
KeywordsComponent (thermodynamics)Cloud computingRemote controlRobotMiddleware (distributed applications)Computer scienceConstruct (python library)Real-time computingSystems Modeling LanguageInternet of ThingsEmbedded systemUnified Modeling LanguageDistributed computingOperating systemArtificial intelligenceComputer networkSoftware

Abstract

fetched live from OpenAlex

This paper proposed a case study of robotization using OpenRTM-AIST for the cloud observation system as Internet of things. We target to construct a remote management system for agricultural settlement sewage treatment facility in this study. We have already constructed cloud based remote observation system for them, and we try to add some remote switch robot on power control panel and several analogue meter observation component. For the robotization, we design a set of robot systems using SysML, especially requirement diagram and component diagram, we also design an actual switch robot using 3D CAD, and we composed some control components on OpenRTM-AIST middleware. From the experimental result, we can provide robotized IoT system and it has been confirmed that multiple locations can be controlled simultaneously.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.239
Teacher spread0.189 · 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