@article{WenningerBayerlSchmidtetal.2019, author = {Wenninger, Marc and Bayerl, Sebastian P. and Schmidt, Jochen and Riedhammer, Korbinian}, title = {Timage - A Robust Time Series Classification Pipeline}, series = {Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science}, volume = {11730}, journal = {Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science}, publisher = {Springer}, address = {Cham}, year = {2019}, abstract = {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.}, language = {en} } @article{WenningerStecherSchmidt2019, author = {Wenninger, Marc and Stecher, Dominik and Schmidt, Jochen}, title = {SVM-Based Segmentation of Home Appliance Energy Measurements}, series = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, journal = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, pages = {1666 -- 1670}, year = {2019}, abstract = {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.}, language = {en} } @book{Schmidt2019, author = {Schmidt, Jochen}, title = {Grundkurs Informatik - Das {\"U}bungsbuch: 148 Aufgaben mit L{\"o}sungen}, edition = {1}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3658259440}, publisher = {Technische Hochschule Rosenheim}, pages = {196}, year = {2019}, language = {de} } @article{NeumayerStecherGrimmetal.2023, author = {Neumayer, Martin and Stecher, Dominik and Grimm, Sebastian and Maier, Andreas and B{\"u}cker, Dominikus and Schmidt, Jochen}, title = {Fault and anomaly detection in district heating substations: A survey on methodology and data sets}, series = {Energy}, volume = {276}, journal = {Energy}, doi = {10.1016/j.energy.2023.127569}, pages = {127569}, year = {2023}, abstract = {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.}, language = {en} } @book{ErnstSchmidtBeneken2023, author = {Ernst, Hartmut and Schmidt, Jochen and Beneken, Gerd}, title = {Grundkurs Informatik: Grundlagen und Konzepte f{\"u}r die erfolgreiche IT-Praxis - Eine umfassende, praxisorientierte Einf{\"u}hrung}, edition = {8}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {9783658417789}, doi = {10.1007/978-3-658-41779-6}, publisher = {Technische Hochschule Rosenheim}, pages = {915}, year = {2023}, abstract = {Das Buch bietet eine umfassende und praxisorientierte Einf{\"u}hrung in die wesentlichen Grundlagen und Konzepte der Informatik. Es umfasst den Stoff, der typischerweise in den ersten Semestern eines Informatikstudiums vermittelt wird, vertieft Zusammenh{\"a}nge, die dar{\"u}ber hinausgehen und macht sie verst{\"a}ndlich. Die Themenauswahl orientiert sich an der langfristigen Relevanz f{\"u}r die praktische Anwendung. Praxisnah und aktuell werden die Inhalte f{\"u}r Studierende der Informatik und verwandter Studieng{\"a}nge sowie f{\"u}r im Beruf stehende Praktiker vermittelt.}, language = {de} } @book{Schmidt2023, author = {Schmidt, Jochen}, title = {Grundkurs Informatik - Das {\"U}bungsbuch: 163 Aufgaben mit L{\"o}sungen}, edition = {3}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3658434427}, doi = {10.1007/978-3-658-43443-4}, publisher = {Technische Hochschule Rosenheim}, pages = {216}, year = {2023}, abstract = {Das Buch richtet sich an Studierende der Informatik oder verwandter Studieng{\"a}nge und enth{\"a}lt {\"U}bungsaufgaben mit L{\"o}sungen aus Gebieten, die typischerweise in den ersten Semestern als Grundlagen behandelt werden. Ausgenommen ist der Bereich des Programmierens. Das Buch erg{\"a}nzt den Grundkurs Informatik mit {\"U}bungen zu ausgew{\"a}hlten Kapiteln, ist aber auch in Kombination mit anderen Lehrb{\"u}chern verwendbar.}, language = {de} }