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Record W4389351758 · doi:10.23977/jaip.2023.060710

Exploration and application of mixed reality technology in modern home design

2023· article· en· W4389351758 on OpenAlexvenueno aff
Qiong Wang

Bibliographic record

VenueJournal of Artificial Intelligence Practice · 2023
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDesign technologyVirtual realityComputer sciencePersonalizationProcess (computing)Key (lock)Engineering design processMixed realityField (mathematics)Augmented realityDesign processVisualizationHuman–computer interactionSystems engineeringEngineering managementEngineeringWork in processOperations managementWorld Wide Web

Abstract

fetched live from OpenAlex

The exploration and application of Mixed Reality (MR) technology in modern home design has become a research field that attracts much attention. MR technology has brought unprecedented innovation opportunities for home design. By integrating virtual elements into the real environment, designers can cooperate with customers in a more interactive and visual way, making it easier for them to understand design concepts and choices. This improves the customization of the design, strengthens the user experience and is expected to improve customer satisfaction. MR technology also plays a key role in the visualization of the design process. Designers can create virtual models to present different design schemes in the actual environment, thus reducing the number of design modifications and saving time and resources. This is expected to improve the efficiency and accuracy of the design. This paper first introduces the concept of MR technology, then summarizes the application of MR technology in modern home design, and finally discusses the challenges and future prospects of MR technology for reference by peers.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.938
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.118
GPT teacher head0.373
Teacher spread0.255 · 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 designOther design
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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