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Prof. Dr. Patrick Glauner

  • Artificial Intelligence
  • Machine Learning, Computer Vision and Natural Language Processing
  • Industry 4.0
  • Quantum Computing
  • Innovation Management

Faculty of Applied Natural Sciences and Industrial Engineering

Campus Cham

Professor

I teach the “Big Data” course. Other than that, I work in the Department of Applied Computer Science.

K 205

0991/3615-453

consulting time

Wednesdays from 12.30 to 1.30pm during the lecture period. Please register by email in advance.

Sortierung:
Beitrag (Sammelband oder Tagungsband)

  • Patrick Glauner

Everyone Needs to Acquire Some Understanding of What AI Is

pg. 267-281.

  • (2021)
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • F. Ünal
  • A. Almalaq
  • S. Ekici
  • Patrick Glauner

Big Data-Driven Detection of False Data Injection Attacks in Smart Meters

In: IEEE Access vol. 9 pg. 144313-144326.

  • (2021)

DOI: 10.1109/ACCESS.2021.3122009

Abstract anzeigen

Today’s energy resources are closer to consumers thanks to sustainable energy and advanced metering infrastructure (AMI), such as smart meters. Smart meters are controlled and manipulated through various interfaces in smart grids, such as cyber, physical and social interfaces. Recently, a large number of non-technical losses (NTLs) have been reported in smart grids worldwide. These are partially caused by false data injections (FDIs). Therefore, ensuring a secure communication medium and protected AMIs is critical to ensuring reliable power supply to consumers. In this paper, we propose a novel Big Data-driven solution that employs machine learning, deep learning and parallel computing techniques. We additionally obtained robust statistical features to detect the FDIs based cyber threats at the distribution level. The performance of the proposed model for NTL detection is investigated using private smart grid datasets in the Turkish distribution network for AMI-level cyber threats, and the results are compared to state-of-the-art machine learning algorithms used for NTL classification problems. Our approach shows promising results, as the accuracy, specificity, and precision metrics of most classifiers are above 90% and false positive rates vary between 0.005 to 0.027.
  • Angewandte Informatik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)

  • L. Trestioreanu
  • Patrick Glauner
  • J. Meira
  • M. Gindt
  • R. State

Using Augmented Reality and Machine Learning in Radiology

In: Innovative Technologies for Market Leadership: Investing in the Future. null (Future of Business and Finance) pg. 89-106.

  • (2020)
  • Angewandte Informatik
  • DIGITAL
  • GESUND
Beitrag (Sammelband oder Tagungsband)

  • M. Thurner
  • Patrick Glauner

Digitalization in Mechanical Engineering

In: Innovative Technologies for Market Leadership: Investing in the Future. null (Future of Business and Finance) pg. 107-117.

  • (2020)
  • Angewandte Informatik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)

  • Patrick Glauner

Unlocking the Power of Artificial Intelligence for Your Business

In: Innovative Technologies for Market Leadership: Investing in the Future. null (Future of Business and Finance) pg. 45-59.

  • (2020)
  • Angewandte Informatik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)

  • Patrick Glauner
  • P. Valtchev
  • R. State

Impact of Biases in Big Data

pg. 645-654.

  • (2018)

Abstract anzeigen

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems.
  • Angewandte Informatik
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)

  • Patrick Glauner
  • N. Dahringer
  • O. Puhachov
  • J. Meira
  • P. Valtchev
  • R. State
  • D. Duarte

Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

  • (2017)

DOI: 10.1109/ICDMW.2017.40

Abstract anzeigen

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • Patrick Glauner
  • J. Meira
  • P. Valtchev
  • R. State
  • F. Bettinger

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

In: International Journal of Computational Intelligence Systems vol. 10 pg. 760-775.

  • (2017)

DOI: 10.2991/ijcis.2017.10.1.51

Abstract anzeigen

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.
  • Angewandte Informatik
  • DIGITAL

Vita

Special achievements:

  • Advised the parliaments of France, Germany, and Luxembourg as an expert witness
  • Ranked by CDO Magazine and Global AI Hub among the worldwide academic data leaders

Positions:

  • Since 2020: Full Professor of Artificial Intelligence, Deggendorf Institute of Technology
  • 2019 - 2020: Head of Data Academy, Alexander Thamm GmbH
  • 2018 - 2019: Innovation Manager for Artificial Intelligence, Krones Group
  • 2018: Visiting Researcher, Université du Québec à Montréal (UQAM)
  • 2015 - 2018: PhD Candidate, University of Luxembourg
  • 2012 - 2014: Fellow, European Organization for Nuclear Research (CERN)

Degrees:

  • 2019: PhD in Computer Science, University of Luxembourg
  • 2018: MBA, Quantic School of Business and Technology
  • 2015: MSc in Machine Learning, Imperial College London
  • 2012: BSc in Computer Science, Karlsruhe University of Applied Sciences

Scholarship:

  • German National Academic Foundation

More information: www.glauner.info