Albertus Alvin Janitra
Albertus Alvin Janitra is a third-year undergraduate student in Bachelor of Advanced Computing majoring in Computer Science and Data Science from University of Sydney. Albertus used to compete and gain some awards in National and International Math Competitions as well as some local competitive programming competitions. He also have some experiences teaching math and competitive programming in his spare time. In addition, he likes to create some creative personal programming projects that are shared to social medias such as Reddit, Youtube, Github, etc. Albertus is interested in Data Science, math related field, robotics, and software development.
Thermal Comfort Assessment from Thermal Images and Ambient Environment Data
Thermal comfort refers to human satisfaction with the thermal environment. Assessing thermal comfort is important since it is related to productivity and health. Office workers who are satisfied with their thermal environment are more productive . Thermal conditions are potentially life-threatening for humans if the core body temperature reaches conditions of hyperthermia or hypothermia . Existing comfort assessment approaches include questionnaire surveys, physiological measurements, and wearable accessories . However, these approaches are either invasive or semi-invasive. In this regard, we use non-invasive thermal images to assess the thermal comfortableness of humans in a confined area. In addition, the ambient environment (e.g., CO2, humidity, illumination, sound) may play a vital role in measuring thermal comfort. This project uses state-of-the-art machine learning algorithms to assess thermal comfort in a room using thermal imaging data and environment data.