Bayesian Optimized ANFIS Network Using Grid Partition and Feature Spectrum for Urban Light Pollution Assessment Article Swipe
Nuoqi Wang
,
Cheng Wang
,
Zhihong Zhao
,
Peiyu Wu
,
Wenqian Xu
,
Bang Qin
,
Dong Wang
,
Rongjun Zhang
,
Qi Yao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jphot.2025.3553420
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jphot.2025.3553420
Precise luminance evaluation of light pollution requires expensive and time-consuming measurement devices such as hyperspectral imaging cameras and imaging luminance-meters, which are also inconvenient to carry. To alleviate this challenge, we selected a low-cost smartphone camera sensor as an alternative tool and investigated the various factors that could influence the accuracy of the measurements by controlling the function of the sensor and assessing its performance, including light illumination conditions and device parameters. Building on this, we have developed an Adaptive Neuro-fuzzy Inference System (ANFIS) structure utilizing global Bayesian optimization and grid partitioning (GP), which integrates the advantages of fuzzy logic in handling data uncertainty with the self-learning capabilities of artificial neural networks. This method can capture the relationships between different parameter combinations while avoiding overfitting, effectively handling unseen data with rapid convergence. The experimental results demonstrate that this method reduces the error by at least 30% compared to conventional methods when tested on a dataset with previously unseen shot parameters. Using a smartphone as a measurement device offers superior portability and broader prospects compared to cameras. Leveraging the powerful processing capabilities of smartphone platforms, we can implement more advanced visualization and computational functions in the future.
Related Topics
Concepts
Computer science
Partition (number theory)
Light pollution
Grid
Adaptive neuro fuzzy inference system
Artificial intelligence
Bayesian probability
Feature (linguistics)
Bayesian network
Pattern recognition (psychology)
Data mining
Environmental science
Machine learning
Fuzzy logic
Mathematics
Fuzzy control system
Optics
Linguistics
Combinatorics
Geometry
Physics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jphot.2025.3553420
- OA Status
- gold
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408710819
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408710819Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jphot.2025.3553420Digital Object Identifier
- Title
-
Bayesian Optimized ANFIS Network Using Grid Partition and Feature Spectrum for Urban Light Pollution AssessmentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-22Full publication date if available
- Authors
-
Nuoqi Wang, Cheng Wang, Zhihong Zhao, Peiyu Wu, Wenqian Xu, Bang Qin, Dong Wang, Rongjun Zhang, Qi YaoList of authors in order
- Landing page
-
https://doi.org/10.1109/jphot.2025.3553420Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/jphot.2025.3553420Direct OA link when available
- Concepts
-
Computer science, Partition (number theory), Light pollution, Grid, Adaptive neuro fuzzy inference system, Artificial intelligence, Bayesian probability, Feature (linguistics), Bayesian network, Pattern recognition (psychology), Data mining, Environmental science, Machine learning, Fuzzy logic, Mathematics, Fuzzy control system, Optics, Linguistics, Combinatorics, Geometry, Physics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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