Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Adverse effects of phthalate, trimellitate, and organophosphate plasticizers on phenotypic and transcriptomic responses in the marine copepod Tigriopus japonicus.

Marine pollution bulletin·2026
Same author

Distribution of LDL-Cholesterol Levels and Their Associations With Cardiovascular Outcomes in Young Adults Under 40 Years in South Korea: A Nationwide Cohort Study.

Journal of lipid and atherosclerosis·2026
Same author

Clinical characteristics and cardiometabolic association of non-suppressed cortisol after dexamethasone suppression test in patients with pheochromocytoma.

European journal of endocrinology·2026
Same author

Steatotic Liver Disease Subtypes and Advanced Fibrosis as Independent Predictors of Ischemic Stroke in Type 2 Diabetes Mellitus.

Endocrinology and metabolism (Seoul, Korea)·2026
Same author

High-Amplitude and Prolonged Glucose Excursions as a Key Determinant of Discordance Between Glucose Management Indicator and Glycated Hemoglobin in Type 1 Diabetes.

Diabetes care·2026
Same author

Income and risk of type 2 diabetes incidence varied associations according to obesity status: a nationwide study.

BMC public health·2026

相关实验视频

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635

基于深度学习使用单个传感器生成实时室内详细照明图的方法

Seung-Taek Oh1, You-Bin Lee2, Jae-Hyun Lim2

  • 1Smart Natural Space Research Center, Kongju National University, Cheonan 31080, Republic of Korea.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

这项研究引入了一种深度学习方法, 用一个传感器创建详细的室内照明图. 这种方法减少了对整合自然光的智能照明系统的传感器需求.

科学领域:

  • 建筑科学
  • 计算机视觉
  • 人工智能

背景情况:

  • 智能照明系统需要准确的室内照明数据,以提高能源效率和用户舒适度.
  • 目前用于绘制室内光线的方法依赖于众多传感器,导致数据处理挑战和用户不便.
  • 深度学习已被用于自然光预测,但对于详细的室内照明分析和传感器减光的应用却较少.

研究的目的:

  • 开发基于深度学习的方法,使用单个传感器生成详细的室内照明图.
  • 解决动态自然光环境中的多传感器系统的局限性.
  • 减少用于准确室内照明分析的传感器数量.

主要方法:

  • 用动态室内照明和太阳位置数据创建了一个数据集.
  • 一个深度神经网络 (DNN) 模型被训练来预测整个室内空间的亮度.
  • 该模型使用单个照度传感器和太阳位置的输入来生成照度图.

主要成果:

  • 提出的方法成功生成了详细的室内照明图.
  • 该系统在晴朗天的平均绝对误差 (MAE) 为2.0卢克,平均绝对百分比误差 (MAPE) 为2.5%.
  • 证明了使用最小传感器计算整个室内区域的照度水平的可行性.
关键词:
深度学习照度地图室内详细照明单个传感器

更多相关视频

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

155
Single Plane Illumination Module and Micro-capillary Approach for a Wide-field Microscope
08:53

Single Plane Illumination Module and Micro-capillary Approach for a Wide-field Microscope

Published on: August 15, 2014

9.8K

相关实验视频

Last Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

635
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

155
Single Plane Illumination Module and Micro-capillary Approach for a Wide-field Microscope
08:53

Single Plane Illumination Module and Micro-capillary Approach for a Wide-field Microscope

Published on: August 15, 2014

9.8K

结论:

  • 单传感器深度学习方法对于创建全面的室内照明图是有效的.
  • 这种方法大大降低了智能照明的硬件需求和数据处理负载.
  • 通过简化传感器基础设施,这些发现支持自然光集成照明技术的商业化.