Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks

SUN Kexue; QU Jiqing

doi:10.11999/JEIT220320

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1766-1773

SUN Kexue, QU Jiqing. Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320

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SUN Kexue, QU Jiqing. Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320

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Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks

doi: 10.11999/JEIT220320

SUN Kexue^{1, 2},
QU Jiqing^{1
,
,}

1.
College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Nation-Local Joint Project Engineering Laboratory of RF Integration & Micropackage, Nanjing 210023, China

Funds: The Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX20_0803), The Natural Science Foundation of Nanjing University of Posts and Telecommunications (NY220013)

Received Date: 2022-03-24
Accepted Date: 2022-08-19
Rev Recd Date: 2022-08-16

Available Online: 2022-08-24

Publish Date: 2023-05-10

Abstract

Abstract

An efficient photodetector placement method using Linear Optimization Fuzzy C-Means (LOFCM) and Artificial Neural Networks (ANN) is proposed for the problem that the current photodetector placement method is computationally intensive, energy intensive, susceptible to human factors and difficult to predict accurately indoor daylight illuminance. In this method, working surface photodetector layout is obtained by the LOFCM algorithm that using Fuzzy C-Means (FCM) to filter data after using Linear Optimization (LO) to sparse weight matrix. Subsequently, a non-linear mathematical model is trained between the working surface photodetector layouts and the four sets of auxiliary photodetector layouts using the ANN respectively. The experimental results show that the proposed LOFCM-based algorithm can reduce the number of working surface photodetector by 37.5% compared to other methods while ensuring the accuracy of calculating the average illumination and uniformity of the working surface. In addition, the wall and window auxiliary photodetector layout obtain a high prediction performance.
- Indoor intelligent lighting,
- Photodetector placement,
- Linear Optimization (LO),
- Fuzzy C-means algorithm (FCM),
- Artificial Neural Networks (ANN)

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References(22)

References

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