TY - JOUR A1 - Wenninger, Marc A1 - Bayerl, Sebastian P. A1 - Schmidt, Jochen A1 - Riedhammer, Korbinian T1 - Timage – A Robust Time Series Classification Pipeline JF - Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science N2 - Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset. KW - neural networks Y1 - 2019 VL - 11730 PB - Springer CY - Cham ER - TY - JOUR A1 - Wenninger, Marc A1 - Stecher, Dominik A1 - Schmidt, Jochen T1 - SVM-Based Segmentation of Home Appliance Energy Measurements JF - Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019 N2 - Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households. KW - Machine Learning Y1 - 2019 SP - 1666 EP - 1670 ER - TY - BOOK A1 - Schmidt, Jochen T1 - Grundkurs Informatik - Das Übungsbuch: 148 Aufgaben mit Lösungen KW - Grundlagen Übungen Y1 - 2019 SN - 978-3658259440 PB - Springer Vieweg CY - Wiesbaden ET - 1 ER - TY - JOUR A1 - Neumayer, Martin A1 - Stecher, Dominik A1 - Grimm, Sebastian A1 - Maier, Andreas A1 - Bücker, Dominikus A1 - Schmidt, Jochen T1 - Fault and anomaly detection in district heating substations: A survey on methodology and data sets JF - Energy N2 - District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks. KW - District heating systems KW - Fault/anomaly detection KW - Machine learning Y1 - 2023 U6 - https://doi.org/10.1016/j.energy.2023.127569 VL - 276 SP - 127569 ER - TY - BOOK A1 - Ernst, Hartmut A1 - Schmidt, Jochen A1 - Beneken, Gerd T1 - Grundkurs Informatik: Grundlagen und Konzepte für die erfolgreiche IT-Praxis – Eine umfassende, praxisorientierte Einführung N2 - Das Buch bietet eine umfassende und praxisorientierte Einführung in die wesentlichen Grundlagen und Konzepte der Informatik. Es umfasst den Stoff, der typischerweise in den ersten Semestern eines Informatikstudiums vermittelt wird, vertieft Zusammenhänge, die darüber hinausgehen und macht sie verständlich. Die Themenauswahl orientiert sich an der langfristigen Relevanz für die praktische Anwendung. Praxisnah und aktuell werden die Inhalte für Studierende der Informatik und verwandter Studiengänge sowie für im Beruf stehende Praktiker vermittelt. KW - Grundlagen Informatik Y1 - 2023 SN - 9783658417789 U6 - https://doi.org/10.1007/978-3-658-41779-6 PB - Springer Vieweg CY - Wiesbaden ET - 8 ER - TY - BOOK A1 - Schmidt, Jochen T1 - Grundkurs Informatik – Das Übungsbuch: 163 Aufgaben mit Lösungen N2 - Das Buch richtet sich an Studierende der Informatik oder verwandter Studiengänge und enthält Übungsaufgaben mit Lösungen aus Gebieten, die typischerweise in den ersten Semestern als Grundlagen behandelt werden. Ausgenommen ist der Bereich des Programmierens. Das Buch ergänzt den Grundkurs Informatik mit Übungen zu ausgewählten Kapiteln, ist aber auch in Kombination mit anderen Lehrbüchern verwendbar. KW - Grundlagen Informatik Y1 - 2023 SN - 978-3658434427 U6 - https://doi.org/10.1007/978-3-658-43443-4 PB - Springer Vieweg CY - Wiesbaden ET - 3 ER -