@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} } @article{StecherNeumayerRamachandranetal.2024, author = {Stecher, Dominik and Neumayer, Martin and Ramachandran, Adithya and Hort, Anastasia and Maier, Andreas and B{\"u}cker, Dominikus and Schmidt, Jochen}, title = {Creating a labelled district heating data set: From anomaly detection towards fault detection}, series = {Energy}, volume = {313}, journal = {Energy}, publisher = {Elsevier}, doi = {10.1016/j.energy.2024.134016}, pages = {134016}, year = {2024}, abstract = {For an efficient operation of district heating systems, being able to detect anomalies and faults at an early stage is highly desirable. Here, data-driven machine learning methods can be a cornerstone, particularly for fault detection in district heating substations, where the availability of heat meter data keeps increasing. However, the creation of data sets suitable for training such machine learning models poses challenges to researchers and practitioners alike. To address this problem, we propose a systematic and domain-specific process for data set creation for fault detection in the form of practical guidelines. This process concretizes the data science and data mining cross-industry standard CRISP-DM for the district heating domain and focuses on the process steps of goal definition, data acquisition and understanding, and data curation. We aim to enable researchers and practitioners to create data sets for fault detection in the district heating domain and therefore also enable the creation or improvement of machine learning models in this domain. In addition, we propose a minimum viable feature set for fault detection in district heating networks with the goal of enabling better cooperation between researchers and easier transfer of the resulting machine learning models, to better proliferate new progress in the field.}, language = {en} } @inproceedings{HambergerMurgulSchmidtetal.2025, author = {Hamberger, Anna and Murgul, Sebastian and Schmidt, Jochen and Heizmann, Michael}, title = {Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription}, series = {Proceedings of the 50th International Computer Music Conference 2025}, booktitle = {Proceedings of the 50th International Computer Music Conference 2025}, publisher = {The International Computer Music Association}, pages = {438 -- 445}, year = {2025}, abstract = {Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.}, language = {en} } @article{StecherZiegltrumReiprichetal.2025, author = {Stecher, Dominik and Ziegltrum, Lukas and Reiprich, Paul and Fuchs, Christian and Maier, Andreas and Schmidt, Jochen}, title = {Neural network synthetic dataset generation for fault detection in district heating substations}, series = {Smart Energy}, volume = {20}, journal = {Smart Energy}, publisher = {Elsevier}, doi = {10.1016/j.segy.2025.100206}, pages = {100206}, year = {2025}, abstract = {District heating systems (DHS) play a vital role in sustainable heating solutions and the decarbonization of the energy sector. However, inefficiencies due to undetected faults in substations result in high return temperatures, increasing heat losses, and limiting the integration of renewable energy sources. The lack of publicly available labeled datasets poses a significant challenge for fault detection using supervised learning models. To address this issue, this study explores three machine learning-based synthetic data generation techniques - time series forecasting, generative adversarial networks (GANs), and fault signature transfer. These methods aim to increase publicly available data either by sharing the generating model or a synthetic dataset. The novelty lies in the combination of advanced supervised machine learning methods being applied to a large, fully labeled data set to create new, equally labeled data for publication, as, to our knowledge, no such dataset has been compiled before. We evaluate our methods on the first-of-its-kind ILSE dataset, which includes real-world smart meter data from 547 substations and 1,162 reviewed faults from a German DHS network, including detailed root cause information. Overall, time series forecasting achieves an MAPE of 3\% to 10\% for inlet and outlet temperature and 25\% to 40\% for heat load and flow rate, both of which are within year-to-year variance. For GANs, specifically TimeGAN, we found a discriminative score of about 0.10 compared to 0.24 in the original publication when tested on Energy benchmark data. Fault signature transfer has yet to yield usable results, most likely due to the high variance in the fault signatures, fault duration, and overlapping or multiple root causes. Finally, fault data in the synthetic data is not yet good enough for practical use, e.g. training a fault detector.}, language = {en} }