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基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法

孙科学 渠吉庆

孙科学, 渠吉庆. 基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法[J]. 电子与信息学报, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320
引用本文: 孙科学, 渠吉庆. 基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法[J]. 电子与信息学报, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320
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
Citation: 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

基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法

doi: 10.11999/JEIT220320
基金项目: 江苏省研究生科研创新计划(KYCX20_0803),南京邮电大学自然科学基金(NY220013)
详细信息
    作者简介:

    孙科学:男,博士,教授,硕士生导师,主要研究方向为智能信号处理与通信软件设计

    渠吉庆:男,硕士生,研究方向为智能信号处理、智能优化与控制

    通讯作者:

    渠吉庆 qjq0806@163.com

  • 中图分类号: TN911.74; TP181

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

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)
  • 摘要: 针对目前光照传感器的布局方式计算量大、能耗高,易受人为因素的影响,难以准确地预测室内日光强度等问题,该文提出一种基于线性优化模糊C均值算法(LOFCM)和人工神经网络(ANN)的光照传感器布局方法。LOFCM算法利用线性优化(LO)稀疏权重矩阵后,使用模糊C均值(FCM)筛选数据,确定工作面光照传感器布局。随后,使用ANN分别训练工作面光照传感器测量值与4组辅助光照传感器测量值之间的非线性数学模型。实验结果表明,该文提出的基于LOFCM算法在保证计算工作面平均照度和均匀度准确的情况下,工作面光照传感器的数量比对比方法减少了37.5%。此外,在4组辅助光照传感器布局中,墙壁和窗户布局方式具有较好的预测精度,为预测室内日光强度提供了更加准确的预测方式。
  • 图  1  LOFCM算法流程图

    图  2  实验房间3维视图

    图  3  工作面光照传感器布局示意图

    图  4  ANN_G1误差频率分布图

    图  5  ANN_G2误差频率分布图

    图  6  ANN_G3误差频率分布图

    图  7  ANN_G4误差频率分布图

    表  1  办公室房间细节

    参数
    空间大小6 m×8 m×3 m
    有效反射率天花板: 0.8; 墙壁: 0.8; 地板: 0.2
    工作面高度0.75 m
    灯具占用高度0.1 m
    下载: 导出CSV

    表  2  优化后的室内光源布局

    $ {N_a} $$ {N_b} $$ {L_a} $(m)$ {L_b} $(m)平均照度
    (lx)
    均匀度眩光度功率密度
    (W/m2)
    342.4882.3265280.89177.325
    下载: 导出CSV

    表  3  不同工作区对传感器有重要影响的灯具表

    区域对于传感器影响较大的灯具
    Z1L1, L2, L5, L6
    Z2L3, L4, L7, L8
    Z3L5, L6, L9, L10
    Z4L7, L8, L11, L12
    下载: 导出CSV

    表  4  工作区Z1结果对比表

    文献选择的光照传感器$ V_{{\text{rec}}}^1 $
    本文$ p_3^1 $,$ p_4^1 $,$ p_{18}^1 $,$ p_{21}^1 $,$ p_{22}^1 $0.078
    文献[19]$ p_3^1 $,$ p_4^1 $,$ p_8^1 $,$ p_{11}^1 $,$ p_{14}^1 $,$ p_{17}^1 $,$ p_{21}^1 $,$ p_{22}^1 $0.074
    下载: 导出CSV

    表  5  工作区Z2-Z4选择的光照传感器

    工作区选择的传感器
    Z2$ p_3^2 $,$ p_4^2 $,$ p_{18}^2 $,$ p_{21}^2 $,$ p_{22}^2 $
    Z3$ p_3^3 $,$ p_4^3 $,$ p_{13}^3 $,$ p_{21}^3 $,$ p_{22}^3 $
    Z4$ p_3^4 $,$ p_4^4 $,$ p_{13}^4 $,$ p_{21}^4 $,$ p_{22}^4 $
    下载: 导出CSV

    表  6  辅助光照传感器分组表

    分组传感器
    G1Wd1,Wd2,Wd3,Wd4
    G2W1W2W3W4
    G3Wd1,Wd2,Wd3,Wd4W1W2
    G4Wd1,Wd2,Wd3,Wd4,W3,W4
    下载: 导出CSV

    表  7  数据采集详细信息表

    采集地点采集时间采集时间间隔天气
    上海6月21-23日7:00-10:00;15:00-18:009月22-24日7:00-10:00;15:00-18:002 min晴天与阴天
    下载: 导出CSV

    表  8  收集数据特征表(lx)

    分组Wd1-Wd4W1-W4Z1Z2Z3Z4
    最小值19.251.5182.0167.0111.0108.0
    最大值19217510681106425359
    平均值45.86122.28474.19505.78244.40245.18
    标准偏差18.0924.85176.94223.9845.9155.90
    下载: 导出CSV

    表  9  神经网络的结果数据对比

    分组MSE(×10–4)Min MSE(×10–4)训练集R2验证集R2测试集R2
    ANN_G13.9301.2390.993970.994190.98667
    ANN_G24.7271.3690.992590.994810.98459
    ANN_G34.1511.6010.991700.991140.98194
    ANN_G46.2281.9740.988470.990060.98686
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-24
  • 修回日期:  2022-08-16
  • 录用日期:  2022-08-19
  • 网络出版日期:  2022-08-24
  • 刊出日期:  2023-05-10

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