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Record W1938312404 · doi:10.5539/mer.v5n2p37

Cableless In-piping Magnetic Actuator for Transportation over Long-range

2015· article· en· W1938312404 on OpenAlexvenueno aff
Tomohiro Izumikawa, Ryuichi Watanabe, Hiroyuki Yaguchi

Bibliographic record

VenueMechanical Engineering Research · 2015
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsActuatorInverterPipingSpring (device)Electrical engineeringMagnetMaterials scienceEngineeringMechanical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper proposes a cableless In-piping magnetic actuator capable of locomotion over long range of 1,000 m within a pipe having an inner diameter of 10 mm. The cableless magnetic actuator is moved by using resonance energy of a mass-spring system excited by using an electromagnetic force. The proposed actuator incorporates a new type of an electrical inverter that directly transforms DC from button batteries into AC. The electrical DC-AC inverter incorporates a mass-spring system, two reed switches and two curved permanent magnets that switch under an electromagnetic force. The conventional DC-AC inverter and the newly proposed inverter were compared, and the effect of the inverter on the motive properties of the cableless magnetic actuator was examined. The influence of the consumption current of the battery on the range of the actuator was examined. Experimental result demonstrates that the cableless magnetic actuator was able to move horizontally at 1,588 m, and horizontal speed at 176.5 mm/s when two reed switches were used.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.083
GPT teacher head0.328
Teacher spread0.245 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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