[[alternative]]Using Machine Learning Models to Improve Detection Performance of Sensors－A Case Study of Air Quality Monitoring Station on Shilin District, Taipei City

[[abstract]]近年來，人類對環境的破壞非常嚴重，導致我們生活的環境也變得更加嚴酷，許多排放的廢棄物經過大地的陽光、雨水等，形成對我們的健康有害的物質。懸浮微粒，PM 就是對全球造成嚴重危害的其中一種，透過我們所排放的碳、氫、氧等化合物在經過上述的條件後而變成了長時間在空氣中殘留，且對呼吸系統造成損傷的有害物質。根據報導，世界 2020年度死於空汙的人數就高達了 16 萬人，台北市因空汙死亡人數也高達 400 人，特別是近年來，除本土的汙染源外，還有從中國隨著季風飄來的大量 PM 2.5，為了讓市民有著更好的生活品質與保護健康，改善汙染源和提早發現這兩項要件是必須的，然而現在市售的感測器還是因售價昂貴而無法普及至全台灣，為了讓感測器普及化，本篇論文建置設備加上人工智慧、AI 模型來進行實驗，目的為提升其偵測效率及提高感測器的穩定度。[[abstract]]In recent years , people damage environment are very serious, resulting in our living environment has also become more severe, many emissions through the earth's sunlight, rain, etc., the formation of substances harmful to our health. Particulate Matter (PM) suspended particulates is one of things that cost serious harm to the world, through our emissions of carbon, hydrogen, oxygen and other compounds after the above-mentioned conditions and carbon， hydrogen, and oxygen compounds that people emit harmful substances that remain in the air for a long time and cause damage to the respiratory system. According to reports, the number of people who died from air pollution in the world in 2020 reached 160,000, and the number of deaths due to air pollution in Taipei City also reached 400 .Specially in recent years, without pollution from , also have a lot of PM 2.5 coming from the other place with the monsoon in recent years. In order to make the sensors popular, this paper build a device with Artificial intelligence(AI) model to conduct experiments in order to improve the detection efficiency and the stability of the sensors