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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.02789 |
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| _version_ | 1866910631979909120 |
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| author | Ochiai, Hideya Hashimoto, Kohki Sakamoto, Takuya Watanabe, Seiya Hara, Ryosuke Yagi, Ryo Aizono, Yuji Esaki, Hiroshi |
| author_facet | Ochiai, Hideya Hashimoto, Kohki Sakamoto, Takuya Watanabe, Seiya Hara, Ryosuke Yagi, Ryo Aizono, Yuji Esaki, Hiroshi |
| contents | Artificial intelligence enables smarter control in building automation by its learning capability of users' preferences on facility control. Reinforcement learning (RL) was one of the approaches to this, but it has many challenges in real-world implementations. We propose a new architecture for logic-free building automation (LFBA) that leverages deep learning (DL) to control room facilities without predefined logic. Our approach differs from RL in that it uses wall switches as supervised signals and a ceiling camera to monitor the environment, allowing the DL model to learn users' preferred controls directly from the scenes and switch states. This LFBA system is tested by our testbed with various conditions and user activities. The results demonstrate the efficacy, achieving 93%-98% control accuracy with VGG, outperforming other DL models such as Vision Transformer and ResNet. This indicates that LFBA can achieve smarter and more user-friendly control by learning from the observable scenes and user interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02789 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera Ochiai, Hideya Hashimoto, Kohki Sakamoto, Takuya Watanabe, Seiya Hara, Ryosuke Yagi, Ryo Aizono, Yuji Esaki, Hiroshi Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics Artificial intelligence enables smarter control in building automation by its learning capability of users' preferences on facility control. Reinforcement learning (RL) was one of the approaches to this, but it has many challenges in real-world implementations. We propose a new architecture for logic-free building automation (LFBA) that leverages deep learning (DL) to control room facilities without predefined logic. Our approach differs from RL in that it uses wall switches as supervised signals and a ceiling camera to monitor the environment, allowing the DL model to learn users' preferred controls directly from the scenes and switch states. This LFBA system is tested by our testbed with various conditions and user activities. The results demonstrate the efficacy, achieving 93%-98% control accuracy with VGG, outperforming other DL models such as Vision Transformer and ResNet. This indicates that LFBA can achieve smarter and more user-friendly control by learning from the observable scenes and user interactions. |
| title | Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2410.02789 |