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Record W2593609351

A new Virtual Reality training system for underground coal mines

2016· article· en· W2593609351 on OpenAlexaff
Hui Zhang, Xueqiu He, Baisheng Nie, Hani S. Mitri

Bibliographic record

Venue3rd International Symposium on Mine Safety Science and Engineering · 2016
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsHeadsetVirtual realityOptical head-mounted displayTraining systemCoal miningComputer scienceTraining (meteorology)Human–computer interactionProjectorEngineeringArtificial intelligenceCoal
DOInot available

Abstract

fetched live from OpenAlex

Virtual Reality (VR) technology in mining is a new field of research. The successful application of VR to mining is critical to mine safety and production. Based on different input/output devices that are used in the VR system, the current VR mining system can be divided into three types: screen-based general system, projector-based customized system, and Head Mount Display (HMD)-based intuitive system. Based on a VR headset, a smartphone and a Leap Motion, an HMD-based intuitive VR training system was built and tested by 10 students who tried both the HMD-based system and a normal screen-based system to experience the difference between the two systems. The results showed that the HMD-based system can give better user experience, and is easier to use. In future, with more intuitive input devices employed, and improved system software, the VR training system for mines could play a much more important role in mine training.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.452

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 science0.0010.000
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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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