Evidence map›Paper›PMID 38660172›Full record

ArticlePeerJ. Computer science2024

Multiangle perception-oriented environmental facility design method based on joint fuzzy decision-making and transfer learning.

Siconghui Yao

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Siconghui YaoCollege of Art and Design, Sias University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In modern society, the demand for environmental facilities is increasing, and how to effectively design and plan environmental facilities has become an urgent issue. However, traditional design methods often consider only certain requirements and perspectives, resulting in design results deviating from the expectations of actual users. In this study, first, perceptual fuzzy decision-making and design transfer learning were selected as methods. Second, by applying multiple perspectives to environmental facility design methods, these two methods were combined, and a new joint algorithm was proposed. Third, when designing environmental facilities, a joint processing framework was constructed considering the impact of human factors, environmental parameters, and cultural value parameters on the design results. Last, the proposed joint algorithm was validated for functionality and satisfaction. The experimental results of this article indicate that in temperature control design, the accuracy of this research model is 17.7-19.6% greater than that of traditional centralized algorithms. In terms of lighting design, the model results of this study are good, with an increase of 16.7-20.2%. This method comprehensively considers the various dimensional requirements of environmental facilities and has good migration performance. In future studies, we will further investigate the applicability of this method in different scenarios and applications to promote the further development of environmental design.

Indexed as

Deep learningEnvironmental facility designFuzzy decision-making methodMethod fusionPerceived multi angle tasksTransfer learning

Identifiers

PMID38660172
PMCPMC11041929

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.