@phdthesis{30670,
  author       = {{Ott, M.}},
  title        = {{{Identifikation und Kompensation produktionsbedingter Abweichungen im elektrischen Antrieb von Elektro- und Hybridfahrzeugen}}},
  doi          = {{10.17619/UNIPB/1-700}},
  year         = {{2019}},
}

@phdthesis{30854,
  author       = {{Bolte, Sven}},
  title        = {{{Modellierung und Optimierung leistungselektronischer Konverter mit Halbleitern großer Bandlücke unter Verwendung kalorimetrischer Messverfahren}}},
  doi          = {{10.17619/UNIPB/1-752}},
  year         = {{2019}},
}

@phdthesis{29903,
  abstract     = {{Hochgeschwindigkeitsantriebe kommen unter anderem in Vakuumpumpen, Turboradialverdichtern und Zentrifugen zum Einsatz und zeichnen sich typischerweise durch einen Betrieb mit konstanter Drehzahl über längere Zeiträume aus. Das in dieser Arbeit betrachtete Antriebssystem setzt sich aus einer permanentmagneterregten Synchronmaschine mit magnetgelagertem Rotor, einem LC-Filter, einem Wechselrichter in 2- oder 3-Stufen-Topologie, diverser Sensorik sowie einem Antriebsregler zusammen.

In der vorliegenden Arbeit wurden Pulsmusterformen auf viertel- und halbschwingungssymmetrischer Basis ausgewählt und nach relevanten Kriterien wie die Eliminierung niederfrequenter Harmonischer oder die Minimierung des Oberschwingungsgehalts offline-optimiert. Hierzu wurde eine modifizierte Variante der Partikel-Schwarm-Optimierung verwendet. Ziel war die Substitution der trägerbasierten Dreiecksmodulation durch eine geeignete Modulatorstruktur zur Generierung und Ausgabe offline-optimierter Pulsmuster an den Wechselrichter. Im Verlauf der Arbeit wurde zudem herausgestellt, dass die herkömmliche feldorientierte Reglerstruktur nicht mit den optimierten Pulsmustern kombiniert werden kann, was auf die fehlende Pulssymmetrie und die abweichenden Kurzzeitmittelwerte der Schaltsignale von der Stellspannung im aktuellen Reglertakt zurückzuführen ist. Daher wurde eine modellbasierte Reglerstruktur, die zum Teil steuernden Charakter aufweist, entworfen und implementiert.

Durchgeführte Messungen an einem Laststand mit einer Bemessungsleistung von 150 kW ergaben im maximal zulässigen Arbeitspunkt gegenüber der dreiecksmodulierten Pulsweitenmodulation eine Steigerung des Gesamtwirkungsgrads von bis zu 0,6% bei Verwendung eines 2-Punkt-Wechselrichters mit ausgangsseitigem LC-Filter. }},
  author       = {{Peter, Klaus}},
  isbn         = {{	978-3-8440-7097-2}},
  publisher    = {{Shaker Verlag}},
  title        = {{{Untersuchung des Verlustverhaltens eines geregelten und mit optimierten Pulsmustern gespeisten Hochgeschwindigkeits-Antriebssystems}}},
  volume       = {{	9}},
  year         = {{2019}},
}

@phdthesis{30855,
  author       = {{Pape, Thorsten}},
  isbn         = {{978-3-8440-6952-5 }},
  title        = {{{Ein Beitrag zur Regelung von Permanentmagnet-Synchronmotoren in Statorflusskoordinaten ohne Rotorlagesensor}}},
  year         = {{2019}},
}

@misc{7720,
  abstract     = {{Die Erfindung betrifft einen optischen Übergang zwischen zwei optischen Schichtwellenleitern. Dazu ist eine Anordnung vorgesehen aus einem ersten optischen Schichtwellenleiter (2) und einem zweiten optischen Schichtwellenleiter (3), wobei der erste optische Schichtwellenleiter (2) und der zweite optische Schichtwellenleiter (3) voneinander verschiedene über ihre jeweilige Länge konstante Dicken (d, r) aufweisen, der erste optische Schichtwellenleiter (2) mit dem zweiten optischen Schichtwellenleiter (3) mittels einer optischen Schichtwellenleiterstruktur (4) verbunden ist, die über ihre gesamte Länge (w) eine Dicke (h) aufweist, die zwischen der Dicke (d) des ersten optischen Schichtwellenleiters (2) und der Dicke (r) des zweiten optischen Schichtwellenleiters (3) liegt. Erfindungsgemäß ist die Dicke (h) der optischen Schichtwellenleiterstruktur (4) über die gesamte Länge (w) der optischen Schichtwellenleiterstruktur (4) konstant. Damit wird eine Möglichkeit für einen effizienten und mit geringen Verlusten behafteten Übergang zwischen zwei optischen Schichtwellenleitern mit unterschiedlicher Dicke bereitgestellt. }},
  author       = {{Hammer, Manfred and Förstner, Jens and Ebers, Lena}},
  keywords     = {{tet_topic_waveguides}},
  pages        = {{9}},
  title        = {{{Optical transition between two optical waveguides layer and method for transmitting light}}},
  year         = {{2019}},
}

@inproceedings{24055,
  abstract     = {{An octave-band voltage-controlled oscillator is phase-locked on the envelope of the pulse train from a mode-locked laser. The locking scheme employs a balanced Mach-Zehnder modulator with two photodiodes as a phase detector. The phase.locked loop has a loop bandwidth of approximately 1MHz and an in-band phase noise of approximately -135dBc/Hz at all frequencies. The integrated jitter from 1kHz to 100MHz is 21fs, 18.3fs and 13.8fs at 5.016GHz, 7.6GHz and 10.032GHz carrier frequencies, respectively. To the authors' knowledge, this is the best jitter performance reported for a PLL with MZM-based phase detection and the first reported PLL of this type featuring an octave-band frequency range.}},
  author       = {{Bahmanian, Meysam and Tiedau, Johannes and Silberhorn, Christine and Scheytt, Christoph}},
  booktitle    = {{2019 International Topical Meeting on Microwave Photonics (MWP)}},
  pages        = {{1--4}},
  title        = {{{Octave-Band Microwave Frequency Synthesizer Using Mode-Locked Laser as a Reference}}},
  doi          = {{10.1109/MWP.2019.8892046}},
  year         = {{2019}},
}

@inproceedings{12918,
  abstract     = {{The test for small delay faults is of major importance for predicting potential early life failures or wearout problems. Typically, a faster-than-at-speed test (FAST) with sev¬eral different frequencies is used to detect also hidden small delays, which can only be propagated over short paths. But then the outputs at the end of long paths may no longer reach their stable values at the nominal observation time and must be considered as unknown (X-values). Thus, test response compaction for FAST must be extremely flexible to cope with high X-rates, which also vary with the test frequencies. Stochastic compaction introduced by Mitra et al. is controlled by weighted pseudo-random signals allowing for easy adaptation to varying conditions. As demonstrated in previous work, the pseudo-random control can be optimized for high fault efficiency or X-reduction, but a given target in fault efficiency cannot be guaranteed. To close this gap, a hybrid space compactor is introduced in this paper. It is based on the observation that many faults are lost in the compaction of relatively few critical test patterns. For these critical patterns a deterministic compaction phase is added to the test, where the existing compactor structure is re-used, but controlled by specifically determined control vectors. }},
  author       = {{Maaz, Mohammad Urf and Sprenger, Alexander and Hellebrand, Sybille}},
  booktitle    = {{50th IEEE International Test Conference (ITC)}},
  keywords     = {{Faster-than-at-speed test, BIST, DFT, Test response compaction, Stochastic compactor, X-handling}},
  location     = {{Washington, DC, USA}},
  pages        = {{1--8}},
  publisher    = {{IEEE}},
  title        = {{{A Hybrid Space Compactor for Adaptive X-Handling}}},
  year         = {{2019}},
}

@article{8634,
  abstract     = {{A rectangular dielectric strip at some distance above an optical slab waveguide is
being considered, for evanescent excitation of the strip through the semi-guided waves supported
by the slab, at specific oblique angles. The 2.5-D configuration shows resonant transmission
properties with respect to variations of the angle of incidence, or of the excitation frequency,
respectively. The strength of the interaction can be controlled by the gap between strip and slab.
For increasing distance, our simulations predict resonant states with unit extremal reflectance
of an angular or spectral width that tends to zero, i.e. resonances with a Q-factor that tends
to infinity, while the resonance position approaches the level of the guided mode of the strip.
This exceptionally simple system realizes what might be termed a “bound state coupled to the
continuum”.}},
  author       = {{Hammer, Manfred and Ebers, Lena and Förstner, Jens}},
  journal      = {{Optics Express}},
  keywords     = {{tet_topic_waveguides}},
  number       = {{7}},
  pages        = {{8}},
  title        = {{{Oblique evanescent excitation of a dielectric strip: A model resonator with an open optical cavity of unlimited Q}}},
  doi          = {{10.1364/OE.27.009313}},
  volume       = {{27}},
  year         = {{2019}},
}

@article{15814,
  abstract     = {{Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital home assistants with a spoken language interface have become a ubiquitous commodity today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound capturing device. The challenges encountered are quite unique and different from many other use cases of automatic speech recognition. The purpose of this tutorial article is to describe, in a way amenable to the non-specialist, the key speech processing algorithms that enable reliable fully hands-free speech interaction with digital home assistants. These technologies include multi-channel acoustic echo cancellation, microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, high-quality speech synthesis, as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all these fields, deep learning has occupied a critical role.}},
  author       = {{Haeb-Umbach, Reinhold and Watanabe, Shinji and Nakatani, Tomohiro and Bacchiani, Michiel and Hoffmeister, Bjoern and Seltzer, Michael L. and Zen, Heiga and Souden, Mehrez}},
  issn         = {{1558-0792}},
  journal      = {{IEEE Signal Processing Magazine}},
  number       = {{6}},
  pages        = {{111--124}},
  title        = {{{Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques}}},
  doi          = {{10.1109/MSP.2019.2918706}},
  volume       = {{36}},
  year         = {{2019}},
}

@inbook{35562,
  author       = {{Schulze Darup, Moritz}},
  booktitle    = {{Privacy in Dynamical Systems}},
  isbn         = {{9789811504921}},
  publisher    = {{Springer Singapore}},
  title        = {{{Encrypted Model Predictive Control in the Cloud}}},
  doi          = {{10.1007/978-981-15-0493-8_11}},
  year         = {{2019}},
}

@inproceedings{35563,
  author       = {{Schulze Darup, Moritz and Book, Gerrit and Giselsson, Pontus}},
  booktitle    = {{2019 18th European Control Conference (ECC)}},
  publisher    = {{IEEE}},
  title        = {{{Towards real-time ADMM for linear MPC}}},
  doi          = {{10.23919/ecc.2019.8796239}},
  year         = {{2019}},
}

@article{35566,
  abstract     = {{<jats:title>Zusammenfassung</jats:title>
               <jats:p>Zukünftige Regelungskonzepte werden verstärkt auf Cloud-Computing und verteiltes Rechnen setzen. In den resultierenden vernetzten Regelungssystemen werden sensible Daten über öffentliche Netzwerke kommuniziert und auf Plattformen Dritter verarbeitet. Verschlüsselte Regelungen zielen darauf ab, die Vertraulichkeit dieser Daten im gesamten Regelkreis zu sichern. Um dieses Ziel zu erreichen, werden klassische Regelungsalgorithmen so modifiziert, dass sie verschlüsselte Regeleingriffe basierend auf verschlüsselten Systemzuständen berechnen. Zum Einsatz kommen dabei homomorphe Verschlüsselungsverfahren, die einfache mathematische Operationen auf verschlüsselten Daten ermöglichen. Der Artikel erläutert die Implementierung verschlüsselter Regelungen anhand von drei wegweisenden Realisierungen in der Cloud.</jats:p>}},
  author       = {{Schulze Darup, Moritz}},
  issn         = {{2196-677X}},
  journal      = {{at - Automatisierungstechnik}},
  keywords     = {{Electrical and Electronic Engineering, Computer Science Applications, Control and Systems Engineering}},
  number       = {{8}},
  pages        = {{668--681}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Verschlüsselte Regelung in der Cloud – Stand der Technik und offene Probleme}}},
  doi          = {{10.1515/auto-2019-0022}},
  volume       = {{67}},
  year         = {{2019}},
}

@inproceedings{35564,
  author       = {{Schulze Darup, Moritz and Teichrib, Dieter}},
  booktitle    = {{2019 18th European Control Conference (ECC)}},
  publisher    = {{IEEE}},
  title        = {{{Efficient computation of RPI sets for tube-based robust MPC}}},
  doi          = {{10.23919/ecc.2019.8796265}},
  year         = {{2019}},
}

@article{35583,
  author       = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}},
  doi          = {{10.1016/j.automatica.2019.108759}},
  volume       = {{113}},
  year         = {{2019}},
}

@article{35584,
  author       = {{Ding, Kemi and Ren, Xiaoqiang and Quevedo, Daniel E. and Dey, Subhrakanti and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Defensive deception against reactive jamming attacks in remote state estimation}}},
  doi          = {{10.1016/j.automatica.2019.108680}},
  volume       = {{113}},
  year         = {{2019}},
}

@article{19450,
  abstract     = {{Wenn akustische Signalverarbeitung mit automatisiertem Lernen verknüpft wird: Nachrichtentechniker arbeiten mit mehreren Mikrofonen und tiefen neuronalen Netzen an besserer Spracherkennung unter widrigsten Bedingungen. Von solchen Sensornetzwerken könnten langfristig auch digitale Sprachassistenten profitieren.}},
  author       = {{Haeb-Umbach, Reinhold}},
  journal      = {{DFG forschung 1/2019}},
  pages        = {{12--15}},
  title        = {{{Lektionen für Alexa & Co?!}}},
  doi          = {{10.1002/fors.201970104}},
  year         = {{2019}},
}

@inproceedings{15237,
  abstract     = {{This  paper  presents  an  approach  to  voice  conversion,  whichdoes neither require parallel data nor speaker or phone labels fortraining.  It can convert between speakers which are not in thetraining set by employing the previously proposed concept of afactorized hierarchical variational autoencoder. Here, linguisticand speaker induced variations are separated upon the notionthat content induced variations change at a much shorter timescale, i.e., at the segment level, than speaker induced variations,which vary at the longer utterance level. In this contribution wepropose to employ convolutional instead of recurrent networklayers  in  the  encoder  and  decoder  blocks,  which  is  shown  toachieve better phone recognition accuracy on the latent segmentvariables at frame-level due to their better temporal resolution.For voice conversion the mean of the utterance variables is re-placed with the respective estimated mean of the target speaker.The resulting log-mel spectra of the decoder output are used aslocal conditions of a WaveNet which is utilized for synthesis ofthe speech waveforms.  Experiments show both good disentan-glement properties of the latent space variables, and good voiceconversion performance.}},
  author       = {{Gburrek, Tobias and Glarner, Thomas and Ebbers, Janek and Haeb-Umbach, Reinhold and Wagner, Petra}},
  booktitle    = {{Proc. 10th ISCA Speech Synthesis Workshop}},
  location     = {{Vienna}},
  pages        = {{81--86}},
  title        = {{{Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion}}},
  doi          = {{10.21437/SSW.2019-15}},
  year         = {{2019}},
}

@inproceedings{15794,
  abstract     = {{In this paper we present our audio tagging system for the DCASE 2019 Challenge Task 2. We propose a model consisting of a convolutional front end using log-mel-energies as input features, a recurrent neural network sequence encoder and a fully connected classifier network outputting an activity probability for each of the 80 considered event classes. Due to the recurrent neural network, which encodes a whole sequence into a single vector, our model is able to process sequences of varying lengths. The model is trained with only little manually labeled training data and a larger amount of automatically labeled web data, which hence suffers from label noise. To efficiently train the model with the provided data we use various data augmentation to prevent overfitting and improve generalization. Our best submitted system achieves a label-weighted label-ranking average precision (lwlrap) of 75.5% on the private test set which is an absolute improvement of 21.7% over the baseline. This system scored the second place in the teams ranking of the DCASE 2019 Challenge Task 2 and the fifth place in the Kaggle competition “Freesound Audio Tagging 2019” with more than 400 participants. After the challenge ended we further improved performance to 76.5% lwlrap setting a new state-of-the-art on this dataset.}},
  author       = {{Ebbers, Janek and Haeb-Umbach, Reinhold}},
  booktitle    = {{DCASE2019 Workshop, New York, USA}},
  title        = {{{Convolutional Recurrent Neural Network and Data Augmentation for Audio Tagging with Noisy Labels and Minimal Supervision}}},
  year         = {{2019}},
}

@inproceedings{15796,
  abstract     = {{In this paper we consider human daily activity recognition using an acoustic sensor network (ASN) which consists of nodes distributed in a home environment. Assuming that the ASN is permanently recording, the vast majority of recordings is silence. Therefore, we propose to employ a computationally efficient two-stage sound recognition system, consisting of an initial sound activity detection (SAD) and a subsequent sound event classification (SEC), which is only activated once sound activity has been detected. We show how a low-latency activity detector with high temporal resolution can be trained from weak labels with low temporal resolution. We further demonstrate the advantage of using spatial features for the subsequent event classification task.}},
  author       = {{Ebbers, Janek and Drude, Lukas and Haeb-Umbach, Reinhold and Brendel, Andreas and Kellermann, Walter}},
  booktitle    = {{CAMSAP 2019, Guadeloupe, West Indies}},
  title        = {{{Weakly Supervised Sound Activity Detection and Event Classification in Acoustic Sensor Networks}}},
  year         = {{2019}},
}

@inbook{48501,
  abstract     = {{<jats:p>Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.</jats:p>}},
  author       = {{Balluff, Stefan and Bendfeld, Jörg and Krauter, Stefan}},
  booktitle    = {{Deep Learning and Neural Networks}},
  publisher    = {{IGI Global}},
  title        = {{{Meteorological Data Forecast using RNN}}},
  doi          = {{10.4018/978-1-7998-0414-7.ch050}},
  year         = {{2019}},
}