School of Engineering Department of Mechanical and Aerospace Engineering 173 Application of AI-based Technique to Enhance Thermal Comfort Sensing for Smart Air Conditioner Supervisor: LEE Yi-Kuen / MAE Student: MO Yin Chung / MAE Course: UROP1100, Summer With the increasing demand for smart buildings, professionals are looking into indoor thermal comfort to cater to people’s needs and consider the energy consumption of buildings. This project focuses on utilizing MEMS sensors to conduct thermal comfort measurements (humidity, air velocity and temperature) in buildings and a smartphone app that consider human factors and activities to evaluate the metabolic rate. By obtaining these data and doing a massive number of surveys, we can determine the Predict Mean Vote (PMV) index and use it to compare with the peoples’ subjective feelings. Moreover, it can also improve the accuracy of Human Thermal Comfort Sensing systems and achieve a smarter Heat Ventilation and AirConditioning system. Application of Artificial Intelligence to Enhance the Fluorescence Microscopy of Circulation Tumor Cells Captured by MEF Chips Supervisor: LEE Yi-Kuen / MAE Student: TANUWIJAYA Richard Valent / SENG Course: UROP1000, Summer Circulating tumour cells (CTCs) in the blood sample can be very useful to identify the current patient’s needs and medications. However, detection and identification of CTCs can be time consuming if done by manual operations. Therefore, a machine to automate the fluorescence microscopy of the captured cancer cells and CTCs is invented. Besides, a program to automate the identification process is needed. Thus, in this report, we will discuss how to detect potential circulating tumour cells (CTCs) in the blood samples with the help of fluorescence microscopy and digital image processing. This report includes the characteristics of circulating tumour cells and white blood cells and explanation of ImageJ macro language program to identify potential CTCs. Application of Artificial Intelligence to Thermal Comfort Sensor App for New-Generation Smart HVAC Systems Supervisor: LEE Yi-Kuen / MAE Student: HUNG Ling Hon / COSC Course: UROP1100, Spring The majority of people today spend most of their time indoors, and the indoor environment can be closely related the people's working efficiency. The Heating Ventilation and AirConditioning (HVAC) systems in buildings provide proper indoor air quality and thermal comfort to people inside. To better provide a thermally comfortable environment, the system needs to refer to a quantitative measure. Predicted Mean Index (PMV) is one of the most commonly used measures of thermal comfort. This research project aims to continuously improve the PMV measuring system proposed by Izhar et al. I have explored an alternative way to get environmental data, explored methods to program and debug a sensor used in the project, and collected more data for one of the algorithms determining people’s metabolic rate.