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|Title:||Smart Small Home Furniture: Smart Outdoor Drying Rack with Shelter|
|Authors:||Lao, Choi Hin|
|Department:||Department of Computer Science|
|Supervisor:||Supervisor: Dr. Chan, Wing Kwong Ricky; First Reader: Dr. Chan, Mang Tang; Second Reader: Prof. Tan, Kay Chen|
|Abstract:||To better utilize space of small home area, most of the Hong Kong residents, especially those live in public housing, have installed drying racks on the external wall of the flat. Other than saving space, this setup can also take the advantage of natural sunshine, letting clothes dry faster and be sanitized thoroughly. However, in case of bad weather, it can make the situation worse. This project aimed to transform a normal mechanical drying rack into a smart one. By adding weather sensors and the connectivity of online weather application programming interface (API), the system can accurately forecast the rain. Once it is detected, a shelter built on top of the rack would expend, covering the clothes underneath. After the rain, the shelter retracts and let the garments expose to fresh air again. In addition, under suitable conditions, the rack would follow the movement of the sun, to archive maximum exposure. Furthermore, by adapting machine learning technique, drying time can also be estimated. On top of this embedded system, the rack also extends its connectivity to users' devices through textual chatbot, allowing them to control the rack's mechanics remotely and obtain the weather condition of their home outdoor environment. The project used serval techniques and algorithms to achieve the above functionalities. Including the use of Kalman's filter to reduce the noise of the raw signal collected from the sensors for more accurate measurement, as well as using ridge regression to implement the machine learning model used for drying time prediction. At the last stage of the project, a working prototype of the system, including the hardware, was provided for demonstration. However, due to lack of data source and difficulties to gather data from the rack, the drying time prediction did not provide satisfactory result. It was hoped that future development of physics and data science would help and break this barrier to make this product truly functional.|
|Appears in Collections:||Computer Science - Undergraduate Final Year Projects |
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