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Record W4405377896 · doi:10.23977/jaip.2024.070406

A Stereo Vision Perception and Control Method for an Intelligent Shift Device

2024· article· en· W4405377896 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Algorithms and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionStereopsisComputer visionComputer scienceArtificial intelligenceControl (management)Depth perceptionPsychology

Abstract

fetched live from OpenAlex

This research has developed an intelligent patient transfer device, designed to enhance the safety and efficiency of patient transfers within healthcare settings. The device integrates advanced multi-sensor fusion localization technology, including LiDAR, Inertial Measurement Unit (IMU), and ultrasonic sensors, along with the Kalman filtering algorithm to improve the precision of motion state estimation, tackling the complexities of state estimation in nonlinear systems. Experimental findings demonstrate that the device has achieved a positioning accuracy of ±1.0 centimeter, a 100% success rate in obstacle avoidance, and motion stability (in terms of acceleration changes) below 0.2 meters/second². These results underscore the exceptional performance of the device in complex medical environments, effectively fulfilling the requirements for safe and efficient patient transfers.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.442

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 head0.046
GPT teacher head0.409
Teacher spread0.363 · 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