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The Design of a Lighting System for Hong Kong International Airport APM Tunnel for Energy Saving with Artificial Intelligence (AI) Lighting Defect Detection System


Affiliations
1 Centre of International Education, Hong Kong College of Technology, Hong Kong
 

The current situation is that global climate change become grimmer, so it has become an important concern. Improving energy efficiency of our device is one of the measures to solve the threat of global climate change. In this circumstance, the first aims research project to design a lighting system to enhance the energy efficiency and the reliability of the lighting system of APM tunnel. Therefore, the replacement of fluorescent light to LED light is an effective way to achieve the target. In truth, substitution of fluorescent lighting to LED light has been promoted for a long time and have a lot of successful examples in the world. It is because LED light has better luminous efficiency to provide similar brightness with less power. In term of reliability, the lifespan of LED light is much higher than fluorescent light. However, the actual lifespan of LED is easily deteriorated by high temperature environment so provide a suitable environment for LED is very important to let LED light reach its theoretical lifespan. Artificial intelligence has been the hottest topic of technology industry in recent years. It has already applied in daily life such as speech recognition in Apple Siri and face recognition in security system. Consequently, the second aims of research project is using artificial intelligence technology to reduce the workload of frontline maintenance staff. The development of lightings inspection system based on ‘Convolutional Neural Network’ technology to analyst the image taken by camera to identify burnt out light tube inside the APM Tunnel.

Keywords

Hong Kong International Airport, LED Lighting, Energy Saving, Artificial Intelligence, Convolutional Neural Network.
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Abstract Views: 230

PDF Views: 126




  • The Design of a Lighting System for Hong Kong International Airport APM Tunnel for Energy Saving with Artificial Intelligence (AI) Lighting Defect Detection System

Abstract Views: 230  |  PDF Views: 126

Authors

Tony Tsang
Centre of International Education, Hong Kong College of Technology, Hong Kong
Chan Shui Hin
Centre of International Education, Hong Kong College of Technology, Hong Kong

Abstract


The current situation is that global climate change become grimmer, so it has become an important concern. Improving energy efficiency of our device is one of the measures to solve the threat of global climate change. In this circumstance, the first aims research project to design a lighting system to enhance the energy efficiency and the reliability of the lighting system of APM tunnel. Therefore, the replacement of fluorescent light to LED light is an effective way to achieve the target. In truth, substitution of fluorescent lighting to LED light has been promoted for a long time and have a lot of successful examples in the world. It is because LED light has better luminous efficiency to provide similar brightness with less power. In term of reliability, the lifespan of LED light is much higher than fluorescent light. However, the actual lifespan of LED is easily deteriorated by high temperature environment so provide a suitable environment for LED is very important to let LED light reach its theoretical lifespan. Artificial intelligence has been the hottest topic of technology industry in recent years. It has already applied in daily life such as speech recognition in Apple Siri and face recognition in security system. Consequently, the second aims of research project is using artificial intelligence technology to reduce the workload of frontline maintenance staff. The development of lightings inspection system based on ‘Convolutional Neural Network’ technology to analyst the image taken by camera to identify burnt out light tube inside the APM Tunnel.

Keywords


Hong Kong International Airport, LED Lighting, Energy Saving, Artificial Intelligence, Convolutional Neural Network.

References