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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Joao Gama

    1. University of Porto, Porto, Portugal

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  2. Sepideh Pashami

    1. Halmstad University, Halmstad, Sweden

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  3. Albert Bifet

    1. Waikato University, Hamilton, New Zealand

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  4. Moamar Sayed-Mouchawe

    1. University of Lille, Lille, France

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  5. Holger Fröning

    1. Heidelberg University, Heidelberg, Germany

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  6. Franz Pernkopf

    1. Graz University of Technology, Graz, Austria

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  7. Gregor Schiele

    1. University of Duisburg-Essen, Essen, Germany

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  8. Michaela Blott

    1. XILINX Research, Dublin, Ireland

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1325)

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About this book

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. 

The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

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Keywords

Table of contents (22 papers)

  1. Front Matter

    Pages I-XVII
    Download chapter PDF

  2. IoT Streams 2020: Stream Learning

    1. Front Matter

      Pages 1-1
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    2. Self Hyper-parameter Tuning for Stream Classification Algorithms

      • Bruno Veloso, João Gama
      Pages 3-13

    3. Challenges of Stream Learning for Predictive Maintenance in the Railway Sector

      • Minh Huong Le Nguyen, Fabien Turgis, Pierre-Emmanuel Fayemi, Albert Bifet
      Pages 14-29

    4. CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw Data of Multiple Sensors

      • Hadi Fanaee-T, Mohamed-Rafik Bouguelia, Mahmoud Rahat, Jonathan Blixt, Harpal Singh
      Pages 30-44

    5. Valve Health Identification Using Sensors and Machine Learning Methods

      • M. Atif Qureshi, Luis Miralles-Pechuán, Jason Payne, Ronan O’Malley, Brian Mac Namee
      Pages 45-60

    6. Failure Detection of an Air Production Unit in Operational Context

      • Mariana Barros, Bruno Veloso, Pedro M. Pereira, Rita P. Ribeiro, João Gama
      Pages 61-74

  3. IoT Streams 2020: Feature Learning

    1. Front Matter

      Pages 75-75
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    2. Enhancing Siamese Neural Networks Through Expert Knowledge for Predictive Maintenance

      • Patrick Klein, Niklas Weingarz, Ralph Bergmann
      Pages 77-92

    3. Explainable Process Monitoring Based on Class Activation Map: Garbage In, Garbage Out

      • Cheolhwan Oh, Junhyung Moon, Jongpil Jeong
      Pages 93-105

    4. AutoML for Predictive Maintenance: One Tool to RUL Them All

      • Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier
      Pages 106-118

    5. Forklift Truck Activity Recognition from CAN Data

      • Kunru Chen, Sepideh Pashami, Sławomir Nowaczyk, Emilia Johansson, Gustav Sternelöv, Thorsteinn Rögnvaldsson
      Pages 119-126

    6. Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles

      • Vandan Revanur, Ayodeji Ayibiowu, Mahmoud Rahat, Reza Khoshkangini
      Pages 127-141

  4. IoT Streams 2020: Unsupervised Machine Learning

    1. Front Matter

      Pages 143-143
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    2. Unsupervised Machine Learning Methods to Estimate a Health Indicator for Condition Monitoring Using Acoustic and Vibration Signals: A Comparison Based on a Toy Data Set from a Coffee Vending Machine

      • Yonas Tefera, Maarten Meire, Stijn Luca, Peter Karsmakers
      Pages 145-159

    3. Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach

      • Pieter Bonte, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck, Sofie Van Hoecke et al.
      Pages 160-172

    4. Interactive Anomaly Detection Based on Clustering and Online Mirror Descent

      • Lingyun Cheng, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, Onur Dikmen
      Pages 173-186

  5. ITEM 2020: Hardware

    1. Front Matter

      Pages 187-187
      Download chapter PDF

    2. hxtorch: PyTorch for BrainScaleS-2

      • Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried, Christian Mauch, Christian Pehle et al.
      Pages 189-200

    3. Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

      • Johannes Weis, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann, Arne Emmel, Eric Müller et al.
      Pages 201-212
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Other volumes

  1. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Editors and Affiliations

  • University of Porto, Porto, Portugal

    Joao Gama

  • Halmstad University, Halmstad, Sweden

    Sepideh Pashami

  • Waikato University, Hamilton, New Zealand

    Albert Bifet

  • University of Lille, Lille, France

    Moamar Sayed-Mouchawe

  • Heidelberg University, Heidelberg, Germany

    Holger Fröning

  • Graz University of Technology, Graz, Austria

    Franz Pernkopf

  • University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  • XILINX Research, Dublin, Ireland

    Michaela Blott

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