Juneseo Chang (Visiting Researcher @ SNU)
Repository Commit HistoryIntroductionFull Bio SketchMr. Chang is currently doing his undergraduate degree in Computer Science and Engineering at Seoul National University, Seoul, Republic of Korea. His research interests include dependable and resilient system design using machine learning approaches. He has also developed a low-power, light-weighted, high-performance machine learning algorithm for human activity data recognition. He is a recipient of the Presidential Science Scholarship funded by the Korea Student Aid Foundation (KOSAF). He was a visiting researcher at the AI-SoC lab as a part of the Daegu Science High School research program from March to November 2018. Research Topic
Smart homes assist users by providing convenient services from activity classification with the help of ML technology. However, most conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure for smart homes’ low-performance hardware. Therefore, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. PublicationsJournal Publications (KCI 1, SCI 1)
Conference Publications (Intl. 3)
Participation in International Conference
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