Lighting design method of museum exhibition hall based on Internet of Things and deep learning

2021 ◽  
pp. 1-13
Author(s):  
Han Chen
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Design Method ◽  
Lighting Design ◽  
Light Sources ◽  
Exhibition Hall ◽  
Environmental Lighting ◽  
Display Content ◽  
Source Form ◽  
Museum Exhibition

In order to improve the lighting effect of the museum exhibition hall, clearly express the exhibition content of the museum exhibition hall, a lighting design method of museum exhibition hall based on Internet of Things and deep learning is proposed. According to the characteristics and functions of light sources and lamps, select appropriate light sources and lamps, and establish a convolutional neural network to evaluate the performance of lighting characteristic network model through computing accuracy, precision, recall and F1 score. Because the illumination of museum exhibition hall cannot be too high, the light projection method is designed to realize the lighting design of museum exhibition hall from two aspects: lighting mode and lighting characteristics, environmental lighting and light source form. The experimental results show that the lighting design method of the museum exhibition hall based on the Internet of Things and deep learning can achieve more than 70%, which has a good lighting effect and can clearly express the display content of the museum exhibition hall.

Parking lot lighting based upon predictions of scene brightness and personal safety

2015 ◽  
Vol 49 (3) ◽  
pp. 293-304 ◽  
Author(s):  
MS Rea ◽  
JD Bullough ◽  
JA Brons
Keyword(s):  
Design Method ◽  
Light Level ◽  
Lighting Design ◽  
Light Sources ◽  
Personal Safety ◽  
Subjective Ratings ◽  
Brightness Perception ◽  
Sense Of Security ◽  
Parking Lot ◽  
Different Levels

Providing subjective impressions of security is central to outdoor lighting design. Current parking lot lighting recommendations are based upon photopic illuminances, regardless of spectrum. Scene brightness perception is directly related to impressions of security, and depends upon both light level and spectrum. A provisional model was used to predict scene brightness for three parking lots, each illuminated to different levels by different light sources. Observers judged scene brightness, security and other factors for each lot. The provisional model accurately predicted both scene brightness and security judgements. The lighting associated with the best subjective ratings also had the lowest power density. A design method using ‘brightness illuminance’ is presented, which can lower system costs while maintaining a sense of security by users.

The Application of LED Lighting in Museum Exhibition Hall

2014 ◽  
Vol 644-650 ◽  
pp. 3449-3452
Author(s):  
Ying Zheng ◽  
Li Wei Huang ◽  
Mi Mi Wang ◽  
Hui Qin Chen ◽  
Li Zhen Zhang
Keyword(s):  
Energy Saving ◽  
Environmental Protection ◽  
Infrared Detection ◽  
Detection Distance ◽  
Led Lighting ◽  
Exhibition Hall ◽  
Dimming Control ◽  
Environmental Lighting ◽  
Museum Exhibition

This paper analyzed the important of lighting in museum from three main aspects, environmental lighting. LED lighting can not only meet requirements of museum lighting, and also has advantage of protecting historical relics and intelligent energy saving. The dimming control of accent lighting is achieved by infrared detection, distance detection of visitor and exhibits. Intelligent lighting and bionic controlling improve the intelligent level of exhibition, protect historical relics effectively, reflect the topic of energy saving and environmental protection.

The Research on the Application of Two Kinds of Light Sources in the Training Venue for Physical Education of Universities

2012 ◽  
Vol 430-432 ◽  
pp. 1786-1790 ◽  
Author(s):  
Shu Fang Li
Keyword(s):  
Energy Efficiency ◽  
High Pressure ◽  
Physical Education ◽  
Lighting Design ◽  
Light Sources ◽  
Metal Halide Lamp ◽  
Sodium Lamp ◽  
Electric Light ◽  
High Pressure Sodium Lamp ◽  
Power And Energy

The energy efficiency experiment of electric light is implemented according to the lighting design of the physical training venues. In the experiment, the corresponding illumination, power and energy efficiency ratio of the commonly used high pressure sodium lamp and metal halide lamp which work under the voltage ranging from 187V to 234V are experimentally measured and the lighting effect characteristics of the two kinds of electric light sources compared, proving that the high pressure sodium light source should be employed in the training venue for physical education of universities.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Deep Learning and Internet of Things based Lung Ailment Recognition through Coughing Spectrograms

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ajay Kumar ◽  
Kumar Abhishek ◽  
Chinmay Chakraborty ◽  
Natalia Kryvinska
Keyword(s):  
Deep Learning ◽  
Internet Of Things
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