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Smart Sensing and Continuous Monitoring

演講摘要

IoT applications are expected to have huge impact to us in a very near future. People imagine Intelligent Transportation Systems, Intelligent Care Systems, smart buildings, or even smart cities may become reality. With smart sensing technology and machine learning algorithms, we are able to understand the environment states and monitor any particular anomalous conditions. Data driven approach becomes a key corner stone for the success of most IoT applications. However, compared to traditional data analytics, data analysis in IoT applications seems to be more challenging simply due to the huge amount of data that can be easily generated by IoT devices in a small period and we have to deal with them using very limited computational resources. In this talk, we introduce our envelope representation for IoT time series data which can be considered as a sparse coding for the time series. With this representation, we are able to deal with IoT data and develop anomaly detection algorithm under the hardware limitations. We will show its applications in monitoring the running machine status and user identification.

講者簡介

李育杰
  • 李育杰 個人網站
  • 交通大學應數系 / 教授
  • 簡介 李育杰教授於 2001 年於美國威斯康辛大學麥迪遜分校取得電腦科學博士後,回 台於中正大學資訊工程系任教一年,轉任台灣科技大學資訊工程系任教,2016 年二月至交通大學應用數學系、數據科學與工程研究所擔任教授。目前也合聘 於清華大學統計研究所與中央研究院資訊創新研究中心。李教授之研究主要在 資料科學、資料探勘與機器學習。在過去十多年間,李教授在監督式學習、半 監督式學習與非監督式學習 (線性 / 非線性維度縮減) 等領域中已發展了相當多的 有效率的學習演算法,並將其應用於網路入侵偵測、網路異常檢測、惡意網址 檢測與合法用戶識別等領域。李教授近年則利用 Online Learning 技術,處理巨量 資料、物聯網安全問題與巨量資料之異常檢測需求等問題。

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