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Record W1578197127

관성센서를 이용한 무인 항공체의 자세 제어에 관한 연구

2008· article· ko· W1578197127 on OpenAlexaboutno aff
오성남, 윤동우, 이검수, 손영익, 김갑일

Bibliographic record

Venue정보 및 제어 심포지엄 논문집 · 2008
Typearticle
Languageko
FieldComputer Science
TopicInternet of Things and Social Network Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Global Positioning SystemGeographyComputer scienceTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

본 논문은 관성센서를 이용한 무인 항공체의 자세 제어에 관한 연구를 다루었다. 항공제의 종류는 크게 고정익기와 회선악기로 나뉘는데 본 연구에서는 회전익기의 형태를 가진 Quarter Vehicle을 사용하였다 Quarter Vehicle은 4개의 프로펠러에 의한 양력과 회전 반발력으로 비행을 한다. 이때의 양력은 수평면에 대해 수직으로 추력을 발생시키므로 다른 비행제보다 불안정하며 이를 안정하게 제어하기 위해 관성 센서를 적용하여 균형을 유지한다. 본 연구에서는 관성센서를 이용하여 UAV의 자세와 균형을 안정적으로 유지하여 안정적인 비행이 가능하도록 하였다. 또한 상호 의존적인 항법 시스템으로 환경변화에 영향을 받지 않으며, 정확한 위치정보를 제공하는 GPS를 사용하여 3개 이상의 위성으로부터 정보를 받아 좌표를 계산하고 위치, 속도 및 방향을 결정하여 자율 비행이 가능하도록 설계 하였다. 본 논문에서는 Quarter Vehicle의 구조와 이론적 배경을 통한 설계, 그리고 관성센서와 GPS의 적용을 위한 방법을 제시 한다.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.010
Scholarly communication0.0110.011
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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
Published2008
Admission routes1
Has abstractyes

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