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1A1-O07 Development of Power Finger : Realization of a Power Amplifier Machine which Accomplishes High Gain and Dexterity in Human Pinching Motion

2007· article· en· W2717022333 on OpenAlexaff
Katsuya Kanaoka, Haruji Nakamura, Takasuke Sonoyama, Takeshi Akamatsu, Go Shirogauchi, Hiromichi Fujimoto, Toshiya INAI, Sadao Kawamura

Bibliographic record

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2007
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsPROTO Manufacturing (Canada)
Fundersnot available
KeywordsAmplifierRealization (probability)Power (physics)Process (computing)Motion (physics)EngineeringComputer scienceMotion controlControl theory (sociology)RobotControl engineeringControl (management)Artificial intelligenceElectronic engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper presents the power amplifier machine which accomplishes high gain and dexterity in human pinching motion. First, the concept of Man-Machine Synergy Effector (MMSE) is introduced from the point of forms of physical interactions among human, robot and surrounding environment. Next, Power Finger, and embodied tool of MMSE in human pinching motion is proposed. The development process of Power Finger is then introduced from basic concepts through mechanical and systematic installation involving control scheme.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2007
Admission routes1
Has abstractyes

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